NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels,  Whistler 2008

NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels, Whistler 2008

12 Lectures · Dec 13, 2008

About

Kernel methods are widely used to address a variety of learning tasks including classification, regression, ranking, clustering, and dimensionality reduction. The appropriate choice of a kernel is often left to the user. But, poor selections may lead to sub-optimal performance. Furthermore, searching for an appropriate kernel manually may be a time-consuming and imperfect art. Instead, the kernel selection process can be included as part of the overall learning problem. In this way, better performance guarantees can be given and the kernel selection process can be made automatic. In this workshop, we will be concerned with using sampled data to select or learn a kernel function or kernel matrix appropriate for the specific task at hand. We will discuss several scenarios, including classification, regression, and ranking, where the use of kernels is ubiquitous, and different settings including inductive, transductive, or semi-supervised learning.

We also invite discussions on the closely related fields of features selection and extraction, and are interested in exploring further the connection with these topics. The goal is to cover all questions related to the problem of learning kernels: different problem formulations, the computational efficiency and accuracy of the algorithms that address these problems and their different strengths and weaknesses, and the theoretical guarantees provided. What is the computational complexity? Does it work in practice? The formulation of some other learning problems, e.g. multi-task learning problems, is often very similar.

These problems and their solutions will also be discussed in this workshop.

More information about workshop - http://www.cs.nyu.edu/learning_kernels

Related categories

Uploaded videos:

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29:52

The Sample Complexity of Learning the Kernel

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Second Order Optimization of Kernel Parameters

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17:22

Learning Sequence Kernels

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Learning with Multiple Similarity Functions

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Multi-Task Learning via Matrix Regularization

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39:03

Feature Selection - From Correlation to Causality

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Mixed Norm Kernels, Hyperkernels and Other Variants

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22:12

Non-sparse Multiple Kernel Learning

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24:48

Infinite Kernel Learning

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Kernel Learning for Novelty Detection

John Shawe-Taylor

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5870 Views

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