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» On learning with dissimilarity functions
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ECML
2004
Springer
14 years 3 months ago
Experiments in Value Function Approximation with Sparse Support Vector Regression
Abstract. We present first experiments using Support Vector Regression as function approximator for an on-line, sarsa-like reinforcement learner. To overcome the batch nature of S...
Tobias Jung, Thomas Uthmann
ISMIR
2004
Springer
145views Music» more  ISMIR 2004»
14 years 3 months ago
Methodological Considerations Concerning Manual Annotation Of Musical Audio In Function Of Algorithm Development
In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of ...
Micheline Lesaffre, Marc Leman, Bernard De Baets, ...
NAACL
2010
13 years 7 months ago
Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
We describe a method of incorporating taskspecific cost functions into standard conditional log-likelihood (CLL) training of linear structured prediction models. Recently introduc...
Kevin Gimpel, Noah A. Smith
ICML
2007
IEEE
14 years 10 months ago
Analyzing feature generation for value-function approximation
We analyze a simple, Bellman-error-based approach to generating basis functions for valuefunction approximation. We show that it generates orthogonal basis functions that provably...
Ronald Parr, Christopher Painter-Wakefield, Lihong...
ML
2002
ACM
140views Machine Learning» more  ML 2002»
13 years 9 months ago
A Probabilistic Framework for SVM Regression and Error Bar Estimation
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. ...
Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin...