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Friday, January 20, 2006 12:00 pm - 12:00 pm EST (GMT -05:00)

AI seminar: Naive Bayes modelling with proper smoothing for information extraction

Speaker: Zhenmei Gu

Information Extraction (IE) summarizes a collection of textual documents into a structual representation by identifying specific facts from text. The naive Bayes model is one of the first statistical models that have been applied to IE for learning extraction patterns from labelled data. In spite of the simplicity and the popularity of the naive Bayes model, we have observed a formulation problem in previous work on naive Bayes IE. In this talk, we present a formal naive Bayes modelling for IE, by which the induced formula for the filler probability estimation is more theoretically sound.

Friday, January 27, 2006 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Segment-based hidden Markov models for information extraction

Speaker: Zhenmei Gu

Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level, i.e., the entire document is modeled by an HMM. This modeling might cause undesired redundancy in extraction in the sense that more than one filler is identified and extracted.

Friday, February 3, 2006 12:00 pm - 12:00 pm EST (GMT -05:00)

AI seminar: Introducing a system for manual citation classification

Speaker: Radoslav Radoulov

Automatic citation classification is a process that associates each citation reference in a scientific paper with one or more previously defined citation functions. Our goal is to build an automatic classifier that looks for features in the text around a citation reference and attempts to classify the citation based on those features. In this seminar I will present our project on automatic citation classification and a system that we will use to collect labeled citations.

Friday, February 10, 2006 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: A sober look at clustering stability

Speaker: David Pal

Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties.

Friday, February 17, 2006 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: An analytic solution to discrete Bayesian reinforcement learning

Speaker: Pascal Poupart

Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is mostly used for offline learning in simulated environments. We propose a new algorithm, called BEETLE, for effective online learning that is computationally efficient while minimizing the amount of exploration.

Friday, March 3, 2006 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Learning the kernels for support vector machines

Speaker: Shai Ben-David

Support Vector machines (SVM's) is one of the most useful and widely applicable machine learning techniques. Each concrete application of SVMs depends on a successful choice of a "kernel matrix". So far, most of the work in this area has focused on developing a variety of kernels and efficient algorithms for employing SVMs with these kernels. Relatively little research attention has been given to the question of how to pick a suitable kernel for any particular learning task at hand.

In this work, we analyze exactly that issue.

Friday, March 10, 2006 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Learning maps

Speaker: Dana Wilkinson

Given a collection of data points annotated with action labels (obtained, for example, from a robot mounted with a web camera or a laser range finder) we wish to learn a representation of the data which can be used as a map (for planning, etc.).