Events

Speaker: Yiyu Yao, University of Regina

As an emerging field of study, granular computing has received much attention. Many models, frameworks, methods and techniques have been proposed and studied. It is perhaps the time to seek for a general and unified view so that fundamental issues can be examined and clarified. This talk examines granular computing from three perspectives.

Speaker: Shady Shehata

Most of text data mining techniques are based on either a word analysis or phrase analysis of the text. The statistical analysis of a term (word or phrase) frequency captures the importance of the term within a document. However, to achieve a more accurate analysis, the underlying mining technique should indicate terms that capture the semantics of the text from which the importance of a term in a sentence and in the document can be derived.

Speaker: Shai Ben-David

Common machine learning theory makes some simplifying assumptions about the learning set up. A problematic such simplification is the assumption that the data available to the learner for training is a faithful representative of the data it will be later tested on.

Speaker: Allan Caine

A CAPTCHA is a special kind of AI hard test to prevent bots from logging into computer systems. We define an AI hard test to be a problem which is intractable for a computer to solve as a matter of general consensus of the AI community.

Using adaptive consultation of experts to improve convergence rates in multiagent learning

Speaker: Greg Hines

In this paper we study the use of experts algorithms in a multiagent setting.

Speaker: Paul Thagard

This talk proposes a theory of how conscious emotional experience is produced by the brain as the result of many interacting brain areas coordinated in working memory.

Speaker: Dana Wilkinson

In a variety of domains it is desirable to learn a representation of an environment defined by a stream of sensori-motor experience. In many cases such a representation is necessary as the observational data is too plentiful to be stored in a computationally feasible way.

Speaker: David Yeung
 

Speaker: Xin Li (Hong Kong Baptist University)

Partially observable Markov decision process (POMDP) is a commonly adopted framework to model planning problems in a stochastic environment. However high dimensionality of POMDP's belief space is still one major cause for making the underlying optimal policy computation intractable.

Speaker: Chun-hung Li (Professor at Hong Kong Baptist University)

In the first part of the talk, we will briefly go over our recent work on the modeling of user participation in online discussion forum.