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Very recently tracking was approached using classification techniques such
as support vector machines. The object to be tracked is discriminated by a
classifier from the background. In a similar spirit we propose a novel on-line
AdaBoost featur
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基于彩色局部二值模式的纹理分类研究，采用LCVBP原理，创新实用-Color texture classification based on local binary pattern studies, using LCVBP prinicple, innovation and practical
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Formulating speech separation as a binary classification
problem has been shown to be effective. While good
separation performance is achieved in matched test conditions
using kernel support vector machines
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A simple and robust scoring technique for binary classification
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Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to examplelabel pair 2) different labels are seldom independent, and label correla
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This paper describes about the support vector machines. It is a pattern recognition mechanism for classification of data.Support vector machine is most commonly used for binary classification
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HEp-2 cell classification using rotation invariant co-occurrence among
local binary patterns
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efficient LBP Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found
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In this paper we present Discriminative Random Fields (DRF), a discrim- inative framework for the classification of natural image regions by incor- porating neighborhood spatial dependencies in the labels as well as the observed data. The proposed mo
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对一个具有150组80个元素的二进制输入矢量P，采用simal.m脚本文件进行自动分类的典型应用程序，其中取r=0.75，竞争层初始节点取10个，对ART1网络进行训练-Has 150 for a group of binary input vector P 80 elements, using simal. M scr ipt file for automatic classification of typical applications, including r = 0.75, the c
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用BP神经网络实现对6维二进制数据的识别和分类，判断其是否为对称模式。-Using BP networks to achieve the identification and classification of six-dimensional binary data, and determining whether it is symmetric mode
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In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such
as whether to use stemming, term frequency versus binary weighting, negation-enr
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