Workshop on Graph Theory and Machine Learning

Workshop on Graph Theory and Machine Learning

12 Lectures · Jun 25, 2007

About

The workshop focuses on the fundamentals of graph theory relevant to learning, with emphasis on the applications of spectral clustering, visualisation and transductive learning.

Methods from graph theory have made an impact in Machine Learning recently through two avenues. The first arises when we view the data samples as the vertices of the graph with the similarity between the examples encoded by the weights on the edges. This view of the data can be used to motivate a number of techniques, including spectral clustering, nonlinear dimensionality reduction, visualisation, transductive and semi-supervised classification.

The second reason for involving graph theory is through the representation of complex objects by graphs. This could be for objects that have a natural graph structure such as molecules or gene networks, or for cases where a feature extraction phase constructs a graph, as for example in natural language processing or computer vision. A key development in this area has been the realisation that feature spaces involving exponentially many features can be used implicitly via kernels that compute in polynomial time inner products between projections into the feature space. This use of graph representations is becoming common in many applications of machine learning making a focus on this topic relevant to a number of application areas, particularly bioinformatics and natural language processing.

For more information visit the Workshop website.

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Uploaded videos:

Invited Speakers

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55:52

Graph methods and geometry of data

Mikhail Belkin

Sep 07, 2007

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9467 Views

Invited Talk
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56:18

A theory of similarity functions for learning and clustering

Avrim Blum

Sep 07, 2007

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9019 Views

Invited Talk

Contributed Talks

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27:38

Convergence of the graph Laplacian application to dimensionality estimation and ...

Jean Yves Audibert

Sep 07, 2007

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5088 Views

Lecture
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30:46

Probabilistic graph partitioning

David Barber

Sep 07, 2007

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6774 Views

Lecture
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27:06

Prediction on a graph

Mark Herbster

Sep 07, 2007

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7583 Views

Lecture
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18:40

Frequent graph mining - what is the question?

Gyorgy Turan

Sep 07, 2007

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6800 Views

Lecture
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20:49

Transductive Rademacher complexities for learning over a graph

Kristiaan Pelckmans

Sep 07, 2007

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4506 Views

Lecture
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20:21

Strings, graphs, invariants

Tomaž Pisanski

Sep 07, 2007

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4923 Views

Lecture
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28:39

On graphical representation of proteins

Milan Randić

Sep 07, 2007

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4570 Views

Lecture
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32:52

Graph complexity for structure and learning

John Shawe-Taylor

Sep 07, 2007

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8994 Views

Lecture
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19:56

Semidefinite ranking on graphs

Shankar Vembu

Sep 07, 2007

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5088 Views

Lecture
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29:44

Random walk graph kernels and rational kernels

S.V.N. Vishwanathan

Sep 07, 2007

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8687 Views

Lecture