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» Results of the KDD'99 Classifier Learning
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NIPS
2004
13 years 10 months ago
Semi-supervised Learning via Gaussian Processes
We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (NCN...
Neil D. Lawrence, Michael I. Jordan
PAMI
2006
205views more  PAMI 2006»
13 years 9 months ago
Learning Outdoor Color Classification
We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training im...
Roberto Manduchi
ICPR
2008
IEEE
14 years 10 months ago
Texture classification with minimal training images
The objective of this work is classifying texture from a single image under unknown lighting conditions. The current and successful approach to this task is to treat it as a stati...
Alireza Tavakoli Targhi, Jan-Mark Geusebroek, Andr...
HAIS
2008
Springer
13 years 10 months ago
Approximate Versus Linguistic Representation in Fuzzy-UCS
Abstract. This paper introduces an approximate fuzzy representation to FuzzyUCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies ...
Albert Orriols-Puig, Jorge Casillas, Ester Bernad&...
JIIS
2006
73views more  JIIS 2006»
13 years 9 months ago
Using KCCA for Japanese-English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm b...
Yaoyong Li, John Shawe-Taylor