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» Results of the KDD'99 Classifier Learning
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NIPS
2004
15 years 2 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»
15 years 1 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
16 years 2 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
15 years 2 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»
15 years 1 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