Speaker: Mu Zhu (Statistics and Actuarial Sciences, University of Waterloo)
I shall describe a few projects in the area of rare target detection.
Speaker: Linli Xu
In this talk, I will discuss a new unsupervised algorithm for training hidden Markov models that is convex and avoids the use of EM.
Speaker: Nasser Mooman
With the increasing amount of available information sources for learners, the need for systems that can effectively and efficiently mine, retrieve, and process such information has become critical.
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.
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.
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.
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.
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.
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.
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.).