# Videos tagged with Computer Science

Lecture slides:Monotony and Surprise Algorithmic and Combinatorial Foundations of Pattern Discovery http://www.cc.gatech.edu/~axa/papers A) Specialihttp://www.cc.gatech.edu/~axa/papers B) Introductory Material Acknowledgements Gill Bejerano Form \x3D FunctionTITLEBioinformatics the Road AheadAt a joint EU \x2D US panel meetingWhich Information AnywayKing Phillip Came Over For Green SoupThe “Chi...

## Suffix tree and Hidden Markov techniques for pattern analysis

Suffix tree construction. Mention the new linear time array constructions - - using suffix trees for finding motifs with gaps (some new observations: 0.5 - 1 hours). - finding cis-regulatory motifs by comparative genomics (1 hour) - Hidden Markov techniques for haplotypingAuthor: Esko Ukkonen, University Of Helsinki

## Statistical Aspects of Pattern Analysis

Abstract: The lectures will introduce the role of statistics in pattern analysis with a discussion of the difference between pattern significance and pattern stability. We will go on to discuss composite hypothesis testing and the Bonferroni correction. Concentration inequalities will be introduced and used to assess the statistical reliability of empirical estimates. We move to consider unifor...

## Fast Best-Effort Pattern Matching in Large Attributed Graphs

We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person’s job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a ‘star’ query would be, “find a CEO who has strong interactions with a Manager, a Lawyer, and an Accountant, or another structure as close to that as possible”. Simila...

## Patterns, Randomness and Information

Information, Complexity, Patterns, Randomness and Compression. And how these ideas can be traced back through Hermann Weyl to Leibniz in 1686, and connect them with Godel & Turing and with the question of how math compares & contrasts with physics and with biology.Author: Gregory Chaitin, University Of Auckland

## A general purpose segmentation algorithm using analytically evaluated random walks

An ideal segmentation algorithm could be applied equally to the problem of isolating organs in a medical volume or to editing a digital photograph without modifying the algorithm, changing parameters, or sacrificing segmentation quality. However, a general-purpose, multiway segmentation of objects in an image/volume remains a challenging problem. In this talk, I will describe a recently develop...

## Nello Cristianini asking Gregory Chaitin about "Pattern"

This is an ultra short interview where the posed question is the one that even Gottfried Wilhelm Leibniz posed to himself "What is the pattern" and what research did Gregory Chaitin on this subject.Interviewer: Nello Cristianini, University Of Bristol Interviewee: Gregory Chaitin, University Of Auckland

## Trajectory Pattern Mining

The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm t...

## Pattern Classification and Large Margin Classifiers

These lectures will provide an introduction to the theory of pattern classification methods. They will focus on relationships between the minimax performance of a learning system and its complexity. There will be four lectures. The first will review the formulation of the pattern classification problem, and several popular pattern classification methods, and present general risk bounds in terms...

## Graphical Models for Structural Pattern Recognition

In the "structural" paradigm for visual pattern recognition, or what some call "strong" pattern recognition, one is not satisfied with simply assigning a class label to an input object, but instead we aim at finding exactly which parts of the template object correspond to which parts of the scene. This is a much harder problem in principle, because it is inherently combinatorial on the number o...