Results for 'machine learning'

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  1.  16
    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.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  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 (...)
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  3.  8
    Fairer Machine Learning in the Real World: Mitigating Discrimination Without Collecting Sensitive Data.Reuben Binns & Michael Veale - 2017 - Big Data and Society 4 (2).
    Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate (...)
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  4.  23
    Machine Learning’s Limitations in Avoiding Automation of Bias.Daniel Varona, Yadira Lizama-Mue & Juan Luis Suárez - 2021 - AI and Society 36 (1):197-203.
    The use of predictive systems has become wider with the development of related computational methods, and the evolution of the sciences in which these methods are applied Solon and Selbst and Pedreschi et al.. The referred methods include machine learning techniques, face and/or voice recognition, temperature mapping, and other, within the artificial intelligence domain. These techniques are being applied to solve problems in socially and politically sensitive areas such as crime prevention and justice management, crowd management, and emotion (...)
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  5.  18
    Machine Learning in Healthcare: Exceptional Technologies Require Exceptional Ethics.Kristine Bærøe, Maarten Jansen & Angeliki Kerasidou - 2020 - American Journal of Bioethics 20 (11):48-51.
    Char et al. describe an interesting and useful approach in their paper, “Identifying ethical considerations for machine learning healthcare applications.” Their proposed framework, which see...
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  6.  8
    Using Machine Learning to Assess Covariate Balance in Matching Studies.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):848-854.
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  7.  61
    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. (...)
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  8.  22
    Using Machine Learning to Predict Decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - 2020 - Artificial Intelligence and Law 28 (2):237-266.
    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 (...)
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  9.  11
    Using Machine Learning to Identify Structural Breaks in Single-Group Interrupted Time Series Designs.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):855-859.
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  10. 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 (...)
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  11. Machines Learning Values.Steve Petersen - forthcoming - In S. Matthew Liao (ed.), Ethics of Artificial Intelligence. New York, USA: Oxford University Press.
  12. 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.
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  13.  15
    Identifying Ethical Considerations for Machine Learning Healthcare Applications.Danton S. Char, Michael D. Abràmoff & Chris Feudtner - 2020 - American Journal of Bioethics 20 (11):7-17.
    Along with potential benefits to healthcare delivery, machine learning healthcare applications raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure th...
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  14.  10
    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 (...)
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  15.  6
    Using machine learning to predict decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - 2020 - Artificial Intelligence and Law 28 (2):237-266.
    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 (...)
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  16.  7
    Machine Learning for Geophysical Characterization of Brittleness: Tuscaloosa Marine Shale Case Study.Mark Mlella, Ming Ma, Rui Zhang & Mehdi Mokhtari - 2020 - Interpretation 8 (3):T589-T597.
    Brittleness is one of the most important reservoir properties for unconventional reservoir exploration and production. Better knowledge about the brittleness distribution can help to optimize the hydraulic fracturing operation and lower costs. However, there are very few reliable and effective physical models to predict the spatial distribution of brittleness. We have developed a machine learning-based method to predict subsurface brittleness by using multidiscipline data sets, such as seismic attributes, rock physics, and petrophysics information, which allows us to implement (...)
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  17.  14
    Combining Machine Learning and Matching Techniques to Improve Causal Inference in Program Evaluation.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):868-874.
  18.  7
    Combining Machine Learning and Propensity Score Weighting to Estimate Causal Effects in Multivalued Treatments.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):875-885.
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  19.  7
    Using Machine Learning to Model Dose-Response Relationships.Ariel Linden, Paul R. Yarnold & Brahmajee K. Nallamothu - 2016 - Journal of Evaluation in Clinical Practice 22 (6):860-867.
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  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 (...)
     
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  21.  9
    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 (...)
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  22. 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 (...)
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  23.  4
    Machine Learning's Feet of Clay.Levente Kriston - 2020 - Journal of Evaluation in Clinical Practice 26 (1):373-375.
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  24.  6
    Machine Learning Healthcare Applications (ML-HCAs) Are No Stand-Alone Systems but Part of an Ecosystem – A Broader Ethical and Health Technology Assessment Approach is Needed.Helene Gerhards, Karsten Weber, Uta Bittner & Heiner Fangerau - 2020 - American Journal of Bioethics 20 (11):46-48.
    ML-HCAs have the potential to significantly change an entire healthcare system. It is not even necessary to presume that this will be disruptive but sufficient to assume that the mere adaptation of...
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  25. 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 (...)
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  26.  23
    Machine Learning and the Future of Realism.Giles Hooker & Cliff Hooker - 2018 - Spontaneous Generations 9 (1):174-182.
  27.  2
    Machine Learning in Psychometrics and Psychological Research.Graziella Orrù, Merylin Monaro, Ciro Conversano, Angelo Gemignani & Giuseppe Sartori - 2020 - Frontiers in Psychology 10.
  28.  2
    What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-Word and Sentence Repetition Tasks.Lina Abed Ibrahim & István Fekete - 2019 - Frontiers in Psychology 9.
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  29.  7
    Using Machine Learning to Evaluate Treatment Effects in Multiple-Group Interrupted Time Series Analysis.Ariel Linden & Paul R. Yarnold - 2018 - Journal of Evaluation in Clinical Practice 24 (4):740-744.
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  30.  6
    Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State.Arkan Al-Zubaidi, Alfred Mertins, Marcus Heldmann, Kamila Jauch-Chara & Thomas F. Münte - 2019 - Frontiers in Human Neuroscience 13.
  31.  9
    Applying Machine Learning to 3D Seismic Image Denoising and Enhancement.Enning Wang & Jeff Nealon - 2019 - Interpretation 7 (3):SE131-SE139.
    We have trained a supervised deep 3D convolutional neural network on marine seismic images for poststack structural seismic image enhancement and noise attenuation. Rather than adding artificial noise to training inputs, the difference in noise levels between the training inputs and labels was created by shot density differences. This design enables the trained CNN to mimic the results and power of stacking to specifically target random and coherent migration artifacts while enhancing low-amplitude reflections. We used field seismic from multiple Gulf (...)
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  32.  11
    Forbidden Knowledge in Machine Learning Reflections on the Limits of Research and Publication.Thilo Hagendorff - forthcoming - AI and Society:1-15.
    Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative (...)
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  33. Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning.Gabbrielle Johnson - forthcoming - Journal Moral Philosophy.
    As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments (...)
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  34.  8
    Accountability in the Machine Learning Pipeline: The Critical Role of Research Ethics Oversight.Melissa D. McCradden, James A. Anderson & Randi Zlotnik Shaul - 2020 - American Journal of Bioethics 20 (11):40-42.
    Char and colleagues provide a useful conceptual framework for the proactive identification of ethical issues arising throughout the lifecycle of machine learning applications in healthcare. Th...
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  35.  12
    Fake News Detection Using Machine Learning Ensemble Methods.Iftikhar Ahmad, Muhammad Yousaf, Suhail Yousaf & Muhammad Ovais Ahmad - 2020 - Complexity 2020:1-11.
    The advent of the World Wide Web and the rapid adoption of social media platforms paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has (...)
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  36.  53
    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, (...)
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  37. 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 (...)
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  38.  18
    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. (...)
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  39. 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.
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  40.  37
    The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - forthcoming - Synthese:1-32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to (...)
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  41. Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.Shalom Lappin with S. Shieber - manuscript
  42.  15
    How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms.Jenna Burrell - 2016 - Big Data and Society 3 (1).
    This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate (...)
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  43.  4
    A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based Internet of Things in Smart Cities.Hang Chen, Sulaiman Khan, Bo Kou, Shah Nazir, Wei Liu & Anwar Hussain - 2020 - Complexity 2020:1-10.
    Generally, the emergence of Internet of Things enabled applications inspired the world during the last few years, providing state-of-the-art and novel-based solutions for different problems. This evolutionary field is mainly lead by wireless sensor network, radio frequency identification, and smart mobile technologies. Among others, the IoT plays a key role in the form of smart medical devices and wearables, with the ability to collect varied and longitudinal patient-generated health data, and at the same time also offering preliminary diagnosis options. In (...)
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  44.  11
    Machine Learning Techniques Applied to Detect Cyber Attacks on Web Applications.M. Chora & R. Kozik - 2015 - Logic Journal of the IGPL 23 (1):45-56.
  45.  16
    Theory Choice, Non-Epistemic Values, and Machine Learning.Ravit Dotan - 2020 - Synthese:1-21.
    I use a theorem from machine learning, called the “No Free Lunch” theorem to support the claim that non-epistemic values are essential to theory choice. I argue that NFL entails that predictive accuracy is insufficient to favor a given theory over others, and that NFL challenges our ability to give a purely epistemic justification for using other traditional epistemic virtues in theory choice. In addition, I argue that the natural way to overcome NFL’s challenge is to use non-epistemic (...)
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  46. 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.
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  47.  7
    Machine Learning Classification of Resting State Functional Connectivity Predicts Smoking Status.Vani Pariyadath, Elliot A. Stein & Thomas J. Ross - 2014 - Frontiers in Human Neuroscience 8.
  48.  20
    Events and Machine Learning.Augustus Hebblewhite, Jakob Hohwy & Tom Drummond - 2021 - Wiley: Topics in Cognitive Science 13 (1):243-247.
    Topics in Cognitive Science, Volume 13, Issue 1, Page 243-247, January 2021.
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  49.  9
    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 (...)
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  50.  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 (...)
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