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» Tangent Distance Kernels for Support Vector Machines
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ML
2007
ACM
144views Machine Learning» more  ML 2007»
13 years 7 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt
ICML
2008
IEEE
14 years 8 months ago
Localized multiple kernel learning
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
Ethem Alpaydin, Mehmet Gönen
NIPS
2003
13 years 9 months ago
Learning a Distance Metric from Relative Comparisons
This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach,...
Matthew Schultz, Thorsten Joachims
ACL
2008
13 years 9 months ago
Kernels on Linguistic Structures for Answer Extraction
Natural Language Processing (NLP) for Information Retrieval has always been an interesting and challenging research area. Despite the high expectations, most of the results indica...
Alessandro Moschitti, Silvia Quarteroni
TRECVID
2008
13 years 9 months ago
ISM TRECVID2008 High-level Feature Extraction
We studied a method using support vector machines (SVMs) with walk-based graph kernels for the high-level feature extraction (HLF) task. In this method, each image is first segmen...
Tomoko Matsui, Jean-Philippe Vert, Shin'ichi Satoh...