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4,176
Ordinal regression by extended binary classification
- In
, 2007
"... We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm ..."
Abstract
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Cited by 38 (4 self)
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We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification
Volume Regularization for Binary Classification
"... We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains “simple ” weight vectors which most of are accurate on the training set. Two ver ..."
Abstract
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Cited by 1 (1 self)
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We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains “simple ” weight vectors which most of are accurate on the training set. Two
Binary Classification Based on Potential Functions
"... Abstract – We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard Support Vector Mach ..."
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Abstract – We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard Support Vector
Ordinal Regression by Extended Binary Classification
"... Abstract We present a reduction framework from ordinal regression to binary classificationbased on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on theextended examples with any binary classification al ..."
Abstract
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Abstract We present a reduction framework from ordinal regression to binary classificationbased on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on theextended examples with any binary classification
Ordinal Regression by Extended Binary Classification
"... Abstract We present a reduction framework from ordinal regression to binary classificationbased on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on theextended examples with any binary classification al ..."
Abstract
- Add to MetaCart
Abstract We present a reduction framework from ordinal regression to binary classificationbased on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on theextended examples with any binary classification
Multiresolution grayscale and rotation invariant texture classification with local binary patterns
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain ..."
Abstract
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Cited by 1299 (39 self)
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This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing
Boosted-PCA for Binary Classification Problems
"... Abstract — In this paper, a Boosted-PCA algorithm is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially ..."
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sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each principal component is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary
Binary Classification Trees for Multi-class Classification Problems
"... This paper proposes a binary classification tree aiming at solving multi-class classification problems using binary classifiers. The tree design is achieved in a way that a class group is partitioned into two distinct subgroups at a node. The node adopts the class-modular scheme to improve the binar ..."
Abstract
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Cited by 5 (0 self)
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This paper proposes a binary classification tree aiming at solving multi-class classification problems using binary classifiers. The tree design is achieved in a way that a class group is partitioned into two distinct subgroups at a node. The node adopts the class-modular scheme to improve
Results 1 - 10
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4,176
