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» On learning with dissimilarity functions
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NIPS
2001
13 years 11 months ago
Kernel Machines and Boolean Functions
We give results about the learnability and required complexity of logical formulae to solve classification problems. These results are obtained by linking propositional logic with...
Adam Kowalczyk, Alex J. Smola, Robert C. Williamso...
PAAMS
2010
Springer
13 years 7 months ago
Improving Functionalities in a Multi-agent Architecture for Ocean Monitoring
This paper presents an improved version of a multiagent architecture aimed at providing solutions for monitoring the interaction between the atmosphere and the ocean. The ocean sur...
Fernando de la Prieta, Óscar Gil, Carolina ...
KDD
2005
ACM
143views Data Mining» more  KDD 2005»
14 years 10 months ago
SVM selective sampling for ranking with application to data retrieval
Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vect...
Hwanjo Yu
NIPS
2008
13 years 11 months ago
Sparse Online Learning via Truncated Gradient
We propose a general method called truncated gradient to induce sparsity in the weights of onlinelearning algorithms with convex loss functions. This method has several essential ...
John Langford, Lihong Li, Tong Zhang
GECCO
2000
Springer
121views Optimization» more  GECCO 2000»
14 years 1 months ago
Metaphor for learning: an evolutionary algorithm
The organizational algorithm is examined as a computational approach to representing interpersonal learning. The structure of the algorithm is introduced and described in context ...
Jody Lee Louse, Alexander Kain, James Hines