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Description: This the implementation of structural SVM for training complex alignment models for protein sequence alignment, especially for homology modeling. The structural SVM algorithm can incorporate many relevant features like secondary structure, relative exposed surface area, profiles and their various interaction into the alignment model. It was developed under Linux and compiles under gcc, built upon the svm^light software by Thorsten Joachims.
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Size: 135082 |
Author: 王强 |
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Description: This the implementation of structural SVM for training complex alignment models for protein sequence alignment, especially for homology modeling. The structural SVM algorithm can incorporate many relevant features like secondary structure, relative exposed surface area, profiles and their various interaction into the alignment model. It was developed under Linux and compiles under gcc, built upon the svm^light software by Thorsten Joachims.
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Size: 135168 |
Author: 王强 |
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Description: SVMhmm: Learns a hidden Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag sequences for new sentences.
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Size: 95232 |
Author: 王强 |
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Description: 3-D SPHIT 压缩Matlab 程序及一些宝贵的MRI 3D序列图-3-D SPHIT compression Matlab procedures and some valuable MRI 3D sequence diagram
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Size: 3563520 |
Author: deepblue0755 |
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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.
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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.
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Size: 117760 |
Author: jon |
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Description: Vsvm3.0是一款window系统下运行的可视化svm算法工具，除了普通的分类和回归算法，还带有多目标回归预测；内含多种参数方式和序列极小化特征选择算法；并且支持多核并行运算-Vsvm3.0 is a window system running svm algorithm visualization tool, in addition to general classification and regression algorithms, but also with a multi-objective regression prediction includes various parameters and sequence minimization methods of feature selection algorithm and to support multi-core parallel computing
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Size: 1162240 |
Author: zj |
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Description: 基于api hook技术的未知病毒检测工具，可以用来学习。使用api hook工具获取刻意进程的api 序列，以api短序为特征输入svm进行识别。-Api hook technique based on the unknown virus detection tool to learn. Tools for use api hook api deliberate process sequence to a short sequence featuring api import svm for recognition.
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Size: 7233536 |
Author: 卜少锋 |
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Description: 支持向量机的混沌是假序列预测研究，支持最小二乘法的向量机-SVM is a false prediction of chaotic sequence, least squares support vector machine
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Size: 2048 |
Author: 123 |
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Description: 包括最小二乘法、SVM、神经网络、1_k近邻法,对球谐函数图形进行仿真,LZ复杂度反映的是一个时间序列中。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Of spherical harmonics graphic simulation, LZ complexity is reflected in a time sequence.
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Size: 7168 |
Author: itpxi |
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Description: 基于SVM的时序序列预测，用python实现，内附测试数据，方便可用。-SVM prediction based on a timing sequence with python to achieve, enclosing the test data to facilitate available.
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Size: 519168 |
Author: 小凌儿 |
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Description: 支持向量机分类程序，使用高斯核函数，SMO顺序最优化算法，为学习SVM提供参考-SVM program, using a Gaussian kernel, SMO sequence optimization algorithm to provide a reference for learning SVM
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Size: 1024 |
Author: 张名 |
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Description: LZ复杂度反映的是一个时间序列中，包括最后计算压缩图像的峰值信噪比和压缩效果的源码,包括最小二乘法、SVM、神经网络、1_k近邻法。- LZ complexity is reflected in a time sequence, Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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Size: 12288 |
Author: ayxtc |
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Description: LZ复杂度反映的是一个时间序列中，包括最小二乘法、SVM、神经网络、1_k近邻法,最大信噪比的独立分量分析算法。- LZ complexity is reflected in a time sequence, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, SNR largest independent component analysis algorithm.
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Size: 6144 |
Author: 赵向 |
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Description: 一种基于序列上升算法的支持向量机实现，硬分割简单版(A support vector machine based on the ascending sequence algorithm, hard to split simple version)
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Size: 1024 |
Author: 356a
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Description: Waveform data analysis, LZ complexity is reflected in a time sequence, Much posture, multi-angle, have different light.
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Size: 58368 |
Author: 王海1
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