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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMsExpand
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Kernel Methods for Pattern Analysis
The lectures will introduce the kernel methods approach to pattern analysis [1] through the particular example of support vector machines for classification. The presentation touches on:Expand
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Learning the Kernel Matrix with Semidefinite Programming
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, byExpand
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Kernel Methods for Pattern Analysis
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general typesExpand
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Large Margin DAGs for Multiclass Classification
We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAGExpand
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Text Classification using String Kernels
We introduce a novel kernel for comparing two text documents. The kernel is an inner product in the feature space consisting of all subsequences of length k. A subsequence is any ordered sequence ofExpand
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On Kernel-Target Alignment
We introduce the notion of kernel-alignment, a measure of similarity between two kernel functions or between a kernel and a target function. This quantity captures the degree of agreement between aExpand
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Support vector machine classification and validation of cancer tissue samples using microarray expression data
MOTIVATION DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differencesExpand
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CAFE: a computational tool for the study of gene family evolution
SUMMARY We present CAFE (Computational Analysis of gene Family Evolution), a tool for the statistical analysis of the evolution of the size of gene families. It uses a stochastic birth and deathExpand
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A statistical framework for genomic data fusion
MOTIVATION During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimentalExpand
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