Graduate studies at Western
|Abstract||Sequential behavior is essential to intelligence, and it is a fundamental part of human activities ranging from reasoning to language, and from everyday skills to complex problem solving. In particular, sequence learning is an important component of learning in many task domains — planning, reasoning, robotics, natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. Naturally, there are many different approaches towards sequence learning, resulting from different perspectives taken in different task domains. These approaches deal with somewhat differently formulated sequential learning problems (for example, some with actions and some without), and/ or different aspects of sequence learning (for example, sequence prediction vs. sequence recognition). Sequence learning is clearly a difiicult task. More powerful algorithms for sequence learning are needed in all of these afore-mentioned domains. It is our view that the right approach to develop better techniques, algorithms, models.|
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Similar books and articles
Axel Cleeremans & L. JimC)nez (1998). Implicit Sequence Learning: The Truth is in the Details. In Michael A. Stadler & Peter A. Frensch (eds.), Handbook of Implicit Learning. Newbury Park, CA: Sage.
Luis Jimenez (2003). Intention, Attention, and Consciousness in Probabilistic Sequence Learning. In Luis Jimenez (ed.), Attention and Implicit Learning. John Benjamins.
Sunbin Song, Howard Jr, James H. & Darlene V. Howard (2007). Implicit Probabilistic Sequence Learning is Independent of Explicit Awareness. Learning and Memory 14 (1-6):167-176.
Axel Cleeremans (1993). Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. MIT Press.
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