Active inference may be defined as Bayesian modeling of a brain with biologically plausible model the agent. Its primary idea relies on free energy principle and prior preference An agent will choose an action that leads to its for future observation. In this paper, we claim active can interpreted using reinforcement learning (RL) algorithms find theoretical connection between them. We extend c...

Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the "under­ complete product of experts" (UPoE), where each expert models a one dimensional pro­ jection of the data. The UPoE may be inter­ preted as a parametric probabilistic model for projection pursuit. Its ML learning rules are identical to the approximate learning rules proposed ...

In recent years, domain-driven data mining (D3M) has received extensive attention in data mining. Unlike the traditional data-driven data mining, D3M tends to discover actionable knowledge by tightly integrating the data mining methods with the domain-specific business processes. However, in most cases, the domain specific actionable knowledge cannot be discovered without the support of domain ...

Experts possess vast knowledge that is typically ignored by standard machine learning methods. This rich, relational knowledge can be utilized to learn more robust models especially in the presence of noisy and incomplete training data. Such experts are often domain but not machine learning experts. Thus, deciding what knowledge to provide is a difficult problem. Our goal is to improve the huma...

BACKGROUND Facilitating motor learning in patients during clinical practice is complex, especially in people with cognitive impairments. General principles of motor learning are available for therapists to use in their practice. However, the translation of evidence from the different fields of motor learning for use in clinical practice is problematic due to lack of uniformity in definition and...

In this paper, we present Committee, a new multi-class learning algorithm related to the Winnow family of algorithms. Committee is an algorithm for combining the predictions of a set of sub-experts in the online mistake-bounded model of learning. A sub-expert is a special type of attribute that predicts with a distribution over a finite number of classes. Committee learns a linear function of s...

Thousands of hours of physical practice substantially change the way movements are performed. The mechanisms underlying altered behavior in highly-trained individuals are so far little understood. We studied experts (handballers) and untrained individuals (novices) in visuomotor adaptation of free throws, where subjects had to adapt their throwing direction to a visual displacement induced by p...

We present a regret-based multiagent learning algorithm which is provably guaranteed to converge (during self-play) to the set of Nash equilibrium in a wide class of games. Our algorithm, FRAME, consults experts in order to obtain strategy suggestions for agents. If the experts provide effective advice for the agent, then the learning process will quickly reach a desired outcome. If, however, t...

Open knowledge communities (OKCs) are computer supported collaborative learning environments that provide opportunities for social knowledge construction, collaboration, participation and communication for ubiquitous learning and informal learning. However, with the rapid expanding of learning content resources and users, it is difficult for learners to find the right persons they need as knowl...