Description: 相似度算法的实现， 在利用支持向量机进行模式分类的时候，有时需要考虑到实时性，为了提高实时性，则利用相似度算法减少样本集个数，从而减少训练时间和支持向量的个数，使得建立起的向量机实时性提高。-Similarity algorithm, support vector machine in pattern classification, it is sometimes need to take into account the real-time, in order to improve real-time, the use of similarity algorithm to reduce the number of sample collection, thus reducing training time and the number of support vectors， improve the real-time of established Vector Machines. Platform: |
Size: 1024 |
Author:牛志嘉 |
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Description: 针对基于GPS／GIS的浮动车数据特点，总结其中无效的数据类型，并给出数据有效处理的方法。以支持向量机原理、交通状态预测方法为基础，分析了常用支持向量回归机、核函数及模型参数的性能，以及各核函数及模型参数对支持向量机性能的影响及作用。针对路段平均速度预测中的小样本、非线性、高维回归等特点，将支持向量回归机方法引入基于浮动车数据的路段车辆速度预测，构建了路段平均速度短时预测模型。并以杭州市某路段的实际数据为例，详细阐述了支持向量回归机预测模型的具体建模和求解过程。运用LibSVM2．84软件包，进行预测模型的参数选择、样本训练以及预测求解，并通过预测结果的对比分析，验证了预测模型的可用性和有效性。-Characteristics of the GPS/GIS-based floating car data, summary of which types of invalid data, and gives the effective data processing method. Support vector machine, the traffic state prediction method based on analysis of commonly used support vector regression machines, nuclear function and performance of the model parameters, as well as the kernel function and model parameters on the performance of SVM and effect. Small sample, nonlinear, high dimensional regression for the prediction of average speed characteristics, the support vector regression machine to the introduction of prediction based on floating car data section of the speed of vehicles, build a short-term forecasting model of average speed. In Hangzhou section of the actual data, for example, elaborated on the specific modeling and solving process of the support vector machine for regression prediction model. Use LibSVM2.84 package forecast model parameter selection, sample training, and forecasting solution, and verif Platform: |
Size: 2207744 |
Author:tangshx |
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Description: 数据挖掘中经典的支持持向量机分类算法的源代码
-Classic support holding the source code for vector machines for classification in data mining Platform: |
Size: 275456 |
Author:luissh |
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Description: 一条关于双正交小波分解的特征提取支持向量量机分类研究论文,可以直接使用。
-A biorthogonal wavelet decomposition feature extraction support the vector vector machines classification research papers, can be used directly. Platform: |
Size: 329728 |
Author:拥抱 |
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Description: 非线性控制系统的支持向量机辨识建模研究
针对非线性控制系统辨识建模难的问题， 系统研究了基于支持向量机的非线性控制系统的辨识建模理论和方法，
然后利用回归型支持向量机( Support Vector Regression， SVR) 设计了一个非线性控制系统的辨识建模系统 仿真试验结果表明， SVR 具有很高的建模精度和较强的泛化能力， 从而验证了该辨识方法的有效性和先进性。-Nonlinear Control Systems Support Vector Machine Identification Modeling modeling for nonlinear control system identification difficult problem, the system studied based on support vector machine identification modeling nonlinear control systems theory and method, and then use the support vector regression machines (Support Vector Regression, SVR) designed a nonlinear control system identification modeling system simulation results showed that, SVR modeling with high accuracy and generalization ability, in order to verify the validity of the identification method and advanced. Platform: |
Size: 390144 |
Author:东土大唐 |
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Description: 深度学习中关于向量机支持向量机（即一种分类器）的相关内容-Depth study on vector machines support vector machine (that is, a classifier) related content Platform: |
Size: 152576 |
Author:舍得 |
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Description: 针对各种机器学习，深度学习领域的一个matlab工具包-A machine learning library focused on deep learning.Following algorithms and models are provided along with some static utility classes:
- Naive Bayes, Linear Regression, Logistic Regression, Softmax Regression, Linear Support Vector Machine, Non-Linear Support Vector Machine (with RBF kernel),
Feed-forward Neural Network, Embedding Neural Network, Convolutional Neural Network, Sparse Autoencoders, Denoising Autoencoders,
Contractive Autoencoders, Stacked Sparse Autoencoders, Self-Taught Learner and Restricted Boltzmann Machines are tested with this version.
- Rest of the methods are not tested hence not supplied and the progress is as follows:
+ Deep Belief Nets with Restricted Boltzmann Machines (not tested)
+ Bayes Nets (tested- refactoring)
+ Hidden Markov Models (tested- refactoring)
+ Conditional Random Fields (work in progress) Platform: |
Size: 346112 |
Author:zhjhe |
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Search - support vector machines