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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: svm和hmm混合模型手写签名认证,充分利用SVM的分类能力以及HMM适合处理连续信号的优势进行手写签名认证-hmm mixed model and svm handwritten signature authentication, make full use of the classification ability of SVM and HMM for continuous signal processing advantages of the handwritten signature verification
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Size: 4217856 |
Author: yqch |
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Description: 隐马尔可夫的经典文献，和通俗易懂的PPT，很好的经典资料，文献常被应用，甚至PPT都经常被模仿-Hidden Markov classic literature, and user-friendly PPT, classic good information, the literature is often applied, or even PPT are often imitate
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Size: 2928640 |
Author: tana |
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Description: 二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机
(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽
略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸
识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem of LDA algorithm can be overcome by two—dimensional LDA f 2DLDA)，and
Support Vector Machine(SVM)has the characteristic of structural risk minimization．In this paper，two methods were
combined and used for face recognition．Firstly，the original images were decomposed into high—frequency and low—frequency
components by Wavelet Transform(WT)．The high—frequency components were ignored，while the low—frequency components
can be obtained．Then．the liner discriminant features were extracted by 2DLDA，and”one VS rest”。strategy of SVMs for
muhiclass classification was chosen to perform face recognition． Experimental results based on ORL f Olivetti Research
Laboratory1 face database and Yale face database show the validity of 2DLDA+SVM algorithm for face recogn ition．
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Size: 236544 |
Author: 费富里 |
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Description: A Speech Recognition System based on a Hybrid HMM/SVM Architecture.A good paper on hybrid model of speech recgnition
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Size: 203776 |
Author: siva |
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Description: 基于HMM和SVM分类器的人脸识别系统，非常强大-Face recognition using Hidden Markov Models (HMM) and SVM features for education and study.
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Size: 4192256 |
Author: LiMingTian |
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Description: 机器学习和模式识别工具包spider。内容很丰富。包含svm 决策树（C45,J48）、svm、knn、adaboost、bagging、hmm（隐马尔科夫模型）、随机树（random forest）等-Machine learning and pattern recognition toolkit spider. Very rich in contents. Tree contains svm (C45, J48), svm, knn, adaboost, bagging, hmm (hidden Markov model), random trees (random forest), etc.
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Size: 4979712 |
Author: |
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Description: 关于隐马尔科夫模型方面的文献资料，介绍了HMM的原理，拓展。创新性将SVM和HMM结合应用-Literature on hidden Markov model aspects, introduced the principle of HMM expand. The innovative application of SVM and HMM combination
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Size: 15212544 |
Author: cc |
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Description: 多种神经网络算法，包括等，可实现模式识别以及回归。-There are many algorithms in the zip,including svm, bp, crf,hmm.It can be used in the pattern recognition.
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Size: 1637376 |
Author: 杨帆 |
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Description: 考虑雨衰 阴影 和多径影响，完整的基于HMM的语音识别系统，包括最小二乘法、SVM、神经网络、1_k近邻法。- Consider shadow rain attenuation and multipath effects Complete HMM-based speech recognition system, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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Size: 4096 |
Author: jou |
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Description: A Hybrid SVM/HMM Acoustic Modeling Approach to Automatic Speech Recognition
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Size: 231424 |
Author: rimi |
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Description: 实现典型相关分析，完整的基于HMM的语音识别系统，包括最小二乘法、SVM、神经网络、1_k近邻法。- Achieve canonical correlation analysis, Complete HMM-based speech recognition system, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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Size: 8192 |
Author: 黄敏军 |
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Description: Complete HMM-based speech recognition system, For feature extraction, signal de-noising, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc.
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Size: 59392 |
Author: 徐在
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Description: Accuracy can reach 98%, Complete HMM-based speech recognition system, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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Size: 151552 |
Author: manjaofienen
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