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
Videos

Feature Selection - From Correlation to Causality
Dec 20, 2008
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8066 views

Multi-Kernel Learning for Biology
Dec 20, 2008
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5239 views

The Sample Complexity of Learning the Kernel
Dec 20, 2008
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4657 views

Mixed Norm Kernels, Hyperkernels and Other Variants
Dec 20, 2008
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5224 views

Learning Sequence Kernels
Dec 20, 2008
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4438 views

Learning Bounds for Support Vector Machines with Learned Kernels
Dec 20, 2008
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2902 views

Learning with Multiple Similarity Functions
Dec 20, 2008
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4180 views

Multi-Task Learning via Matrix Regularization
Dec 20, 2008
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3265 views

Kernel Learning for Novelty Detection
Dec 20, 2008
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5880 views

Infinite Kernel Learning
Dec 20, 2008
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5054 views

Non-sparse Multiple Kernel Learning
Dec 20, 2008
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4261 views

Second Order Optimization of Kernel Parameters
Dec 20, 2008
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4610 views