Description: 为了更准确地识别人的表情，在识别人脸7 种基本表情（愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶）时，采用了局域二值
模式技术提取面部特征，进行由粗略到精细的表情分类。在粗略分类阶段，7 种基本表情中的2 种表情被选为初步分类结果（候选表情）。
在精细分类阶段，选用计算加权卡方值确定最终分类结果。采用日本的Jaffe 表情数据库来验证算法性能，对陌生人表情的识别率为77.9％，
其结果优于采用同样数据库的其他方法，且易于实现-In order to more accurately identify the person s facial expressions, in the identification of seven kinds of basic facial expressions (anger, disgust, fear, happy, expressionless, sadness and surprise), the use of local binary pattern of facial features extraction techniques, carried out by the rough to the fine expression classification. In the rough classification stage, the seven kinds of basic expressions in the two kinds of expression was selected as the initial classification results (candidate expressions). In the fine classification stage, the choice of calculating the weighted chi-square value to determine the final classification results. Jaffe expressions used in Japan to validate algorithm performance database of strangers face recognition rate was 77.9, the result is better than using the same database in other ways, and are easy to achieve Platform: |
Size: 212992 |
Author:张波 |
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Description: 留一模型选择法leave-one-out model selection，适合支持向量机分类和回归时进行参数选择。-looms uses a slightly modified
BSVM to perform model selection on binary classification problems.
Currently the RBF kernel is supported. Platform: |
Size: 56320 |
Author:Mountain |
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Description: 实现纹理模式的LBP特征表示及分类。
实现一种基于局部二值模式LBP（Local Binary Pattern）的多分辨率灰度尺度及旋转不变性的纹理分类方法-LBP texture model to achieve that as well as the breakdown characteristics. The realization of a model based on local binary LBP (Local Binary Pattern) Multiresolution gray-scale and rotation invariant texture classification Platform: |
Size: 3072 |
Author:张阳军 |
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Description: 经典的最大似然法分类法的C语言实现，有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file.
Note: For constructong variance-covariance matrix must be generic binary file.
Platform: |
Size: 4096 |
Author:李会利 |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
Platform: |
Size: 109568 |
Author:jon |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
Platform: |
Size: 117760 |
Author:jon |
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Description: Bayes classification is on of data at employed for binary classification in first level and it can be extended for multi level of selection Platform: |
Size: 1024 |
Author:Madhavaraja |
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Description: ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
-ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
Platform: |
Size: 4096 |
Author:saadat |
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Description: 论文提出一种全自动识别人脸七种基本表情(愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊奇)的方法。该方法首先
采用几何模型匹配法自动定位出人眼，在此基础上进行人脸大小归一化，然后利用局域二值模式(Local Binary Pattern．
LBP)技术提取面部特征，最后采用由粗到细的方案进行表情分类。采用日本的JAFFE公用表情数据库来检测算法的性
能，实验结果验证了方法的有效性。-Paper proposes a fully automatic identification of seven basic facial expressions (anger, disgust, fear, happy, neutral, sadness, and surprise) method. In this method, the geometric model matching method with automatic positioning to human eyes, in this based on the human face of Size Normalization and then use local binary pattern (Local Binary Pattern. LBP) Jishu extract facial features, and finally using coarse-to- fine program expression classification. Public expression of Japan' s JAFFE database performance detection algorithm, experimental results verify the validity of the method. Platform: |
Size: 171008 |
Author:MJ |
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Description: A Novel Efficient Approach for Audio Segmentation
a novel approach to audio segmentation
is presented. The problem of detecting audio segments’
limits is treated as a binary classification task.
Frames are classified as “segment limits” vs “nonsegment
limits”. For each audio frame a spectrogram
is computed and eight feature values are extracted from
respective frequency bands. Final decisions are taken
based on a classifier combination scheme Platform: |
Size: 225280 |
Author:kvga |
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Description: Bayes分类器——算法设计
1． 使用决策树(Decision tree)分类算法、朴素贝叶斯（Naï ve Bayes）算法或者K-近邻（kNN）算法（三者任选其一）对给定的训练数据集构造分类器，并在测试数据集上进行分类预测。
2． 数据集描述：
Tic-tac-toe游戏的二叉分类。Tic-tac-toe游戏示例如下-Bayes classifier- Algorithm 1. Using the decision tree (Decision tree) classification algorithm, Naive Bayes (Naï ve Bayes) algorithm or K-nearest neighbor (kNN) algorithm (choose any one of three) on a given set of training data classification structure, and the test data Classification and Prediction on the set. 2. Data set description: Tic-tac-toe game binary classification. Tic-tac-toe game example is as follows Platform: |
Size: 1439744 |
Author:vera |
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Description: This program is the program implementing the neural network.
It is the program which implements the binary classification by using the neural network.
i used with opencv and vc-This program is the program implementing the neural network.
It is the program which implements the binary classification by using the neural network.
i used with opencv and vc++ Platform: |
Size: 60416 |
Author:pattern |
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Description: 数据挖掘中分类算法贝叶斯算法，用于二分类问题-Data mining classification algorithm of bayesian algorithm is used for binary classification problem Platform: |
Size: 2048 |
Author:赵月 |
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Description: 此程序是实现二元分类器，为在统计学习中最基本的程序。-This program is a binary classifier for statistical learning the most basic procedures. Platform: |
Size: 5120 |
Author:Tony Shao |
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Search - binary classification