Results for 'machine learning'

1000+ found
Order:
See also
  1. Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  2. Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  3.  67
    Machine learning, inductive reasoning, and reliability of generalisations.Petr Spelda - 2018 - AI and Society 35 (1):29-37.
    The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper (...)
    Direct download (3 more)  
    Translate
     
     
    Export citation  
     
    Bookmark  
  4.  31
    Phronesis and Automated Science: The Case of Machine Learning and Biology.Emanuele Ratti - 2019 - In Fabio Sterpetti & M. Bertolaso (eds.), Will Science Remain Human? Springer.
    The applications of machine learning and deep learning to the natural sciences has fostered the idea that the automated nature of algorithmic analysis will gradually dispense human beings from scientific work. In this paper, I will show that this view is problematic, at least when ML is applied to biology. In particular, I will claim that ML is not independent of human beings and cannot form the basis of automated science. Computer scientists conceive their work as being (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  5.  93
    Argument Based Machine Learning Applied to Law.Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko - 2005 - Artificial Intelligence and Law 13 (1):53-73.
    In this paper we discuss the application of a new machine learning approach – Argument Based Machine Learning – to the legal domain. An experiment using a dataset which has also been used in previous experiments with other learning techniques is described, and comparison with previous experiments made. We also tested this method for its robustness to noise in learning data. Argumentation based machine learning is particularly suited to the legal domain as (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  6. Introduction: Machine Learning as Philosophy of Science.Kevin B. Korb - 2004 - Minds and Machines 14 (4):433-440.
    I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.
    Direct download (10 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  7.  10
    A New Tangible User Interface for Machine Learning Document Review.Caroline Privault, Jacki O’Neill, Victor Ciriza & Jean-Michel Renders - 2010 - Artificial Intelligence and Law 18 (4):459-479.
    This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost, time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the intelligence of the reviewers and enabling collaborative work.
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  8.  51
    Machine Learning by Imitating Human Learning.Chang Kuo-Chin, Hong Tzung-Pei & Tseng Shian-Shyong - 1996 - Minds and Machines 6 (2):203-228.
    Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark  
  9. Inductive Logic, Verisimilitude, and Machine Learning.Ilkka Niiniluoto - 2005 - In Petr H’Ajek, Luis Vald’es-Villanueva & Dag Westerståhl (eds.), Logic, methodology and philosophy of science. London: College Publications. pp. 295/314.
    This paper starts by summarizing work that philosophers have done in the fields of inductive logic since 1950s and truth approximation since 1970s. It then proceeds to interpret and critically evaluate the studies on machine learning within artificial intelligence since 1980s. Parallels are drawn between identifiability results within formal learning theory and convergence results within Hintikka’s inductive logic. Another comparison is made between the PAC-learning of concepts and the notion of probable approximate truth.
     
    Export citation  
     
    Bookmark  
  10. Clinical Applications of Machine Learning Algorithms: Beyond the Black Box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
  11.  9
    Learning From Peers’ Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning.Michał Król & Magdalena Król - 2019 - Cognitive Science 43 (2):e12716.
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark  
  12. Philosophy and Machine Learning.Paul Thagard - 1990 - Canadian Journal of Philosophy 20 (2):261-76.
    This article discusses the philosophical relevance of recent computational work on inductive inference being conducted in the rapidly growing branch of artificial intelligence called machine learning.
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  13. Machine Learning and the Foundations of Inductive Inference.Francesco Bergadano - 1993 - Minds and Machines 3 (1):31-51.
    The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  14.  74
    Algorithmic Decision-Making Based on Machine Learning From Big Data: Can Transparency Restore Accountability?Paul B. de Laat - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   6 citations  
  15.  15
    Algorithmic Decision-Making Based on Machine Learning From Big Data: Can Transparency Restore Accountability?Massimo Durante & Marcello D'Agostino - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated algorithms (...)
    No categories
    Direct download  
     
    Export citation  
     
    Bookmark   4 citations  
  16.  34
    Against Interpretability: A Critical Examination of the Interpretability Problem in Machine Learning.Maya Krishnan - forthcoming - Philosophy and Technology:1-16.
    The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a “black box problem.” That is, machine learning algorithms are “opaque” to human users, failing to be “interpretable” or “explicable” in terms that would render categorization procedures “understandable.” The purpose of this paper is to challenge (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  17.  35
    Mechanistic Models and the Explanatory Limits of Machine Learning.Emanuele Ratti & Ezequiel López-Rubio - unknown
    We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  18.  13
    “The Brain Is the Prisoner of Thought”: A Machine-Learning Assisted Quantitative Narrative Analysis of Literary Metaphors for Use in Neurocognitive Poetics.M. Jacobs Arthur & Kinder Annette - 2017 - Metaphor and Symbol 32 (3):139-160.
    Two main goals of the emerging field of neurocognitive poetics are the use of more natural and ecologically valid stimuli, tasks and contexts and providing methods and models allowing to quantify distinctive features of verbal materials used in such tasks and contexts and their effects on readers responses. A natural key element of poetic language, metaphor, still is understudied insofar as relatively little empirical research looked at literary or poetic metaphors. An exception is Katz et al.’s corpus of 204 literary (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  19. Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar. StuartmShieber - unknown
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments (...)
    Translate
     
     
    Export citation  
     
    Bookmark   6 citations  
  20. Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.Shalom Lappin - unknown
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments (...)
     
    Export citation  
     
    Bookmark   7 citations  
  21.  41
    Machine Learning: A Structuralist Discipline?Christophe Bruchansky - 2019 - AI and Society 34 (4):931-938.
    Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  22.  17
    From Privacy to Anti-Discrimination in Times of Machine Learning.Thilo Hagendorff - 2019 - Ethics and Information Technology 21 (4):331-343.
    Due to the technology of machine learning, new breakthroughs are currently being achieved with constant regularity. By using machine learning techniques, computer applications can be developed and used to solve tasks that have hitherto been assumed not to be solvable by computers. If these achievements consider applications that collect and process personal data, this is typically perceived as a threat to information privacy. This paper aims to discuss applications from both fields of personality and image analysis. (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  23.  28
    The Epistemic Importance of Technology in Computer Simulation and Machine Learning.Michael Resch & Andreas Kaminski - 2019 - Minds and Machines 29 (1):9-17.
    Scientificity is essentially methodology. The use of information technology as methodological instruments in science has been increasing for decades, this raises the question: Does this transform science? This question is the subject of the Special Issue in Minds and Machines “The epistemological significance of methods in computer simulation and machine learning”. We show that there is a technological change in this area that has three methodological and epistemic consequences: methodological opacity, reproducibility issues, and altered forms of justification.
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  24.  4
    Machine Learning: A Structuralist Discipline?Christophe Bruchansky - 2019 - AI and Society 34 (4):931-938.
    Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  25.  26
    Word Associations Contribute to Machine Learning in Automatic Scoring of Degree of Emotional Tones in Dream Reports.Reza Amini, Catherine Sabourin & Joseph De Koninck - 2011 - Consciousness and Cognition 20 (4):1570-1576.
    Scientific study of dreams requires the most objective methods to reliably analyze dream content. In this context, artificial intelligence should prove useful for an automatic and non subjective scoring technique. Past research has utilized word search and emotional affiliation methods, to model and automatically match human judges’ scoring of dream report’s negative emotional tone. The current study added word associations to improve the model’s accuracy. Word associations were established using words’ frequency of co-occurrence with their defining words as found in (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  26.  13
    Using Machine Learning to Predict Decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - forthcoming - Artificial Intelligence and Law:1-30.
    When courts started publishing judgements, big data analysis within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  27.  8
    Interperforming in AI: Question of ‘Natural’ in Machine Learning and Recurrent Neural Networks.Tolga Yalur - forthcoming - AI and Society:1-9.
    This article offers a critical inquiry of contemporary neural network models as an instance of machine learning, from an interdisciplinary perspective of AI studies and performativity. It shows the limits on the architecture of these network systems due to the misemployment of ‘natural’ performance, and it offers ‘context’ as a variable from a performative approach, instead of a constant. The article begins with a brief review of machine learning-based natural language processing systems and continues with a (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  28.  11
    Seismic Structure Interpretation Based on Machine Learning: A Case Study in Coal Mining.Dong Li, Suping Peng, Yongxu Lu, Yinling Guo & Xiaoqin Cui - 2019 - Interpretation 7 (3):SE69-SE79.
    Interpretation of geologic structures entails ambiguity and uncertainties. It usually requires interpreter judgment and is time consuming. Deep exploitation of resources challenges the accuracy and efficiency of geologic structure interpretation. The application of machine-learning algorithms to seismic interpretation can effectively solve these problems. We analyzed the theory and applicability of five machine-learning algorithms. Seismic forward modeling is a key connection between the model and seismic response, and it can obtain seismic data of known geologic structures. Based (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  29.  10
    Doubt and the Algorithm: On the Partial Accounts of Machine Learning.Louise Amoore - 2019 - Theory, Culture and Society 36 (6):147-169.
    In a 1955 lecture the physicist Richard Feynman reflected on the place of doubt within scientific practice. ‘Permit us to question, to doubt, to not be sure’, proposed Feynman, ‘it is possible to live and not to know’. In our contemporary world, the science of machine learning algorithms appears to transform the relations between science, knowledge and doubt, to make even the most doubtful event amenable to action. What might it mean to ‘leave room for doubt’ or ‘to (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  30.  2
    Modeling Investor Behavior Using Machine Learning: Mean-Reversion and Momentum Trading Strategies.Thiago Christiano Silva, Benjamin Miranda Tabak & Idamar Magalhães Ferreira - 2019 - Complexity 2019:1-14.
    We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high-frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  31. Machine Learning and the Cognitive Basis of Natural Language.Shalom Lappin - unknown
    Machine learning and statistical methods have yielded impressive results in a wide variety of natural language processing tasks. These advances have generally been regarded as engineering achievements. In fact it is possible to argue that the success of machine learning methods is significant for our understanding of the cognitive basis of language acquisition and processing. Recent work in unsupervised grammar induction is particularly relevant to this issue. It suggests that knowledge of language can be achieved through (...)
    Translate
     
     
    Export citation  
     
    Bookmark   2 citations  
  32.  9
    Using Machine Learning as an Aid to Seismic Geomorphology, Which Attributes Are the Best Input?Lennon Infante-Paez & Kurt J. Marfurt - 2019 - Interpretation 7 (3):SE1-SE18.
    Volcanic rocks with intermediate magma composition indicate distinctive patterns in seismic amplitude data. Depending on the processes by which they were extruded to the surface, these patterns may be chaotic, moderate-amplitude reflectors or continuous high-amplitude reflectors. We have identified appropriate seismic attributes that highlight the characteristics of such patterns and use them as input to self-organizing maps to isolate these volcanic facies from their clastic counterpart. Our analysis indicates that such clustering is possible when the patterns are approximately self-similar, such (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  33.  9
    Machine Learning to Reduce Cycle Time for Time-Lapse Seismic Data Assimilation Into Reservoir Management.Yang Xue, Mariela Araujo, Jorge Lopez, Kanglin Wang & Gautam Kumar - 2019 - Interpretation 7 (3):SE123-SE130.
    Time-lapse seismic is widely deployed in offshore operations to monitor improved oil recovery methods including water flooding, yet its value for enhanced well and reservoir management is not fully realized due to the long cycle times required for quantitative 4D seismic data assimilation into dynamic reservoir models. To shorten the cycle, we have designed a simple inversion workflow to estimate reservoir property changes directly from 4D attribute maps using machine-learning methods. We generated tens of thousands of training samples (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  34.  14
    Model Theory and Machine Learning.Hunter Chase & James Freitag - 2019 - Bulletin of Symbolic Logic 25 (3):319-332.
    About 25 years ago, it came to light that a single combinatorial property determines both an important dividing line in model theory and machine learning. The following years saw a fruitful exchange of ideas between PAC-learning and the model theory of NIP structures. In this article, we point out a new and similar connection between model theory and machine learning, this time developing a correspondence between stability and learnability in various settings of online learning. (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  35.  8
    Assessment of Machine-Learning Techniques in Predicting Lithofluid Facies Logs in Hydrocarbon Wells.Saba Keynejad, Marc L. Sbar & Roy A. Johnson - 2019 - Interpretation 7 (3):SF1-SF13.
    Wireline log interpretation is a well-exercised procedure in the oil and gas industry with all its added value from exploration to production stages. It becomes even more important when it is one of only a few available alternatives to compensate for the lack of core samples in a study of lithologic and fluid variations in a well. Yet, as with other purely expert-oriented interpretational techniques, there is always a considerable risk of subjective or technical errors. We have adopted a hybrid (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  36.  45
    Statistical Machine Learning and the Logic of Scientific Discovery.Antonino Freno - 2009 - Iris. European Journal of Philosophy and Public Debate 1 (2):375-388.
    One important problem in the philosophy of science is whether there can be a normative theory of discovery, as opposed to a normative theory of justification. Although the possibility of developing a logic of scientific discovery has been often doubted by philosophers, it is particularly interesting to consider how the basic insights of a normative theory of discovery have been turned into an effective research program in computer science, namely the research field of machine learning. In this paper, (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  37. Using Machine Learning for Non-Sentential Utterance Classification.Jonathan Ginzburg & Shalom Lappin - unknown
    In this paper we investigate the use of machine learning techniques to classify a wide range of non-sentential utterance types in dialogue, a necessary first step in the interpretation of such fragments. We train different learners on a set of contextual features that can be extracted from PoS information. Our results achieve an 87% weighted f-score—a 25% improvement over a simple rule-based algorithm baseline.
    Translate
     
     
    Export citation  
     
    Bookmark   1 citation  
  38.  6
    A Machine-Learning Benchmark for Facies Classification.Yazeed Alaudah, Patrycja Michałowicz, Motaz Alfarraj & Ghassan AlRegib - 2019 - Interpretation 7 (3):SE175-SE187.
    The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely, the absence of large publicly available annotated data sets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes or use different train and test splits. In addition, it is common for papers that apply machine learning for facies (...)
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  39. Varieties of Justification in Machine Learning.David Corfield - 2010 - Minds and Machines 20 (2):291-301.
    Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.
    Direct download (10 more)  
     
    Export citation  
     
    Bookmark  
  40.  5
    Machine-Learning Algorithm for Estimating Oil-Recovery Factor Using a Combination of Engineering and Stratigraphic Dependent Parameters.Kachalla Aliyuda & John Howell - 2019 - Interpretation 7 (3):SE151-SE159.
    The methods used to estimate recovery factor change through the life cycle of a field. During appraisal, prior to development when there are no production data, we typically rely on analog fields and empirical methods. Given the absence of a perfect analog, these methods are typically associated with a wide range of uncertainty. During plateau, recovery factors are typically associated with simulation and dynamic modeling, whereas in later field life, once the field drops off the plateau, a decline curve analysis (...)
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  41.  1
    Using machine learning to predict decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - forthcoming - Artificial Intelligence and Law:1-30.
    When courts started publishing judgements, big data analysis within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning (...)
    No categories
    Direct download (2 more)  
    Translate
     
     
    Export citation  
     
    Bookmark  
  42.  65
    Combining Psychological Models with Machine Learning to Better Predict People’s Decisions.Avi Rosenfeld, Inon Zuckerman, Amos Azaria & Sarit Kraus - 2012 - Synthese 189 (S1):81-93.
    Creating agents that proficiently interact with people is critical for many applications. Towards creating these agents, models are needed that effectively predict people's decisions in a variety of problems. To date, two approaches have been suggested to generally describe people's decision behavior. One approach creates a-priori predictions about people's behavior, either based on theoretical rational behavior or based on psychological models, including bounded rationality. A second type of approach focuses on creating models based exclusively on observations of people's behavior. At (...)
    Direct download (5 more)  
     
    Export citation  
     
    Bookmark  
  43.  4
    Machine Learning Regressors and Their Metrics to Predict Synthetic Sonic and Mechanical Properties.Ishank Gupta, Deepak Devegowda, Vikram Jayaram, Chandra Rai & Carl Sondergeld - 2019 - Interpretation 7 (3):SF41-SF55.
    Planning and optimizing completion design for hydraulic fracturing require a quantifiable understanding of the spatial distribution of the brittleness of the rock and other geomechanical properties. Eventually, the goal is to maximize the stimulated reservoir volume with minimal cost overhead. The compressional and shear velocities can also be used to calculate Young’s modulus, Poisson’s ratio, and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthetic sonic logs. We have (...)
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  44.  5
    Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning.Mireille Hildebrandt - 2019 - Theoretical Inquiries in Law 20 (1):83-121.
    This Article takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy, I expand previous work on the relational nature of privacy, and the productive indeterminacy of human identity it implies, into an ecological understanding of privacy, (...)
    No categories
    Direct download  
     
    Export citation  
     
    Bookmark  
  45.  20
    An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality.Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John Aronis, Bruce G. Buchanon, Richard Caruana, Michael J. Fine, Clark Glymour, Geoffrey Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom Mitchell, Thomas Richardson & Peter Spirtes - unknown
    This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model’s potential (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  46.  89
    A Dynamic Interaction Between Machine Learning and the Philosophy of Science.Jon Williamson - 2004 - Minds and Machines 14 (4):539-549.
    The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark  
  47.  19
    An Approach for Generating Pattern-Based Shorthand Using Speech-to-Text Conversion and Machine Learning.H. K. Anasuya Devi & K. R. Abhinand - 2013 - Journal of Intelligent Systems 22 (3):229-240.
    Rapid handwriting, popularly known as shorthand, involves writing symbols and abbreviations in lieu of common words or phrases. This method increases the speed of transcription and is primarily used to record oral dictation. Someone skilled in shorthand will be able to write as fast as the dictation occurs, and these patterns are later transliterated into actual, natural language words. A new kind of rapid handwriting scheme is proposed, called the Pattern-Based Shorthand. A word on a keyboard involves pressing a unique (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  48.  30
    Nonmonotonic Reasoning , Argumentation and Machine Learning 1 Introduction.Peter Clark - 1990 - Argumentation:1-11.
    Machine learning and nonmonotonic reasoning are closely related, both concerned with making plausible as well as certain inferences based on available data. In this document a brief overview of different approaches to nonmonotonic reasoning is presented, and it is shown how the concept of argumentation systems arises. The relationship with machine learning work is also discussed. The document aims to highlight the links between nonmonotonic reasoning, argumentation and machine learning and as a result propose (...)
    Direct download  
     
    Export citation  
     
    Bookmark  
  49.  66
    The Philosophy of Science and its Relation to Machine Learning.Jon Williamson - unknown
    In this chapter I discuss connections between machine learning and the philosophy of science. First I consider the relationship between the two disciplines. There is a clear analogy between hypothesis choice in science and model selection in machine learning. While this analogy has been invoked to argue that the two disciplines are essentially doing the same thing and should merge, I maintain that the disciplines are distinct but related and that there is a dynamic interaction operating (...)
    Direct download  
     
    Export citation  
     
    Bookmark  
  50.  11
    Predicting the Ideological Orientation During the Spanish 24M Elections in Twitter Using Machine Learning.Ronaldo Cristiano Prati & Elias Said-Hung - 2019 - AI and Society 34 (3):589-598.
    Through the application of machine learning techniques, this paper aims to estimate the importance of messages with ideological load during the elections held in Spain on May 24th, 2015 posted by Twitter’s users, as well as other variables associated with the publication of these types of messages. Our study collected and analysed 24,900 tweets associated to two of the main trending topics’ hashtags used in the election day and build a predictive model to infer the ideological orientation for (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
1 — 50 / 1000