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» Evaluating learning algorithms and classifiers
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112
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PAMI
2008
270views more  PAMI 2008»
15 years 13 days ago
Randomized Clustering Forests for Image Classification
This paper introduces three new contributions to the problems of image classification and image search. First, we propose a new image patch quantization algorithm. Other competitiv...
Frank Moosmann, Eric Nowak, Frédéric...
ICPR
2010
IEEE
14 years 10 months ago
Data Classification on Multiple Manifolds
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside ...
Rui Xiao, Qijun Zhao, David Zhang, Pengfei Shi
81
Voted
IDEAL
2009
Springer
14 years 10 months ago
Optimizing Data Transformations for Classification Tasks
Many classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (...
José María Valls, Ricardo Aler
114
Voted
ICDAR
2011
IEEE
14 years 3 days ago
Objective Function Design for MCE-Based Combination of On-line and Off-line Character Recognizers for On-line Handwritten Japane
—This paper describes effective object function design for combining on-line and off-line character recognizers for on-line handwritten Japanese text recognition. We combine on-l...
Bilan Zhu, Jinfeng Gao, Masaki Nakagawa
ML
2002
ACM
141views Machine Learning» more  ML 2002»
15 years 5 days ago
On the Existence of Linear Weak Learners and Applications to Boosting
We consider the existence of a linear weak learner for boosting algorithms. A weak learner for binary classification problems is required to achieve a weighted empirical error on t...
Shie Mannor, Ron Meir