The ability to detect and learn about the predictive relations existing between events in the world is essential for adaptive behavior. It allows us to use past events to predict the future and to adjust our behavior accordingly. Pavlovian fear conditioning allows anticipation of sources of danger in the environment. It guides attention away from poorer predictors toward better predictors of da...

Use of nonlinear feature maps via kernel approximation has led to success in many online learning tasks. As a popular kernel approximationmethod, Nyström approximation, has beenwell investigated, and various landmark points selection methods have been proposed to improve the approximation quality. However, these improved Nyström methods cannot be directly applied to the online learning setting ...

Research on learning has for historical reasons been divided mainly into behaviourally oriented studies performed on animals and more cognitively oriented studies in humans (Anderson, 2000). Accordingly, studies aimed at the presumed neurophysiological basis of learning have either exploited the full range of cognitive-phenomenological approaches in humans while being methodologically confined ...

This paper presents an online unsupervised learning algorithm to flexibly adapt the speaker-independent (SI) hidden Markov models (HMM’s) to new speaker. We apply the quasi-Bayes (QB) estimate to incrementally obtain word sequence and adaptation parameters for adjusting HMM’s once a block of unlabeled data is enrolled. Accordingly, the nonstationary statistics of varying speakers can be success...

Computer war strategy games offer a challenging domain for AI techniques for learning because they involve multiple players, the world in the games is only partially observable and the state space is extremely large. Model-based reflection and self-adaptation is one method for learning in such a complex domain. In this method, the game-playing agent contains a model of its own reasoning process...

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Bayesian networks are ...

With the rapid expansion of e-commerce, trust has become a central research topic in online environment for its key role in affecting e-commerce success. Our study focuses on the initial online trust building for brick-and-click companies. Building upon social learning theory, we propose a framework to examine the learning processes and important antecedents to online trust building. To demonst...

Slater JA, Lujan HL, DiCarlo SE. Does gender influence learning style preferences of first-year medical students? Adv Physiol Educ 31: 336–342, 2007; doi:10.1152/advan.00010.2007.—Students have specific learning style preferences, and these preferences may be different between male and female students. Understanding a student’s learning style preference is an important consideration when design...