搜索资源列表
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用rbf神经网络实现分类和曲线拟合，包括分离器和曲线拟合两个文件，可以直接解压缩使用-using neural network classification and curve fitting, including separator and curve fitting two documents, decompression can be used directly
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一个包含丰富内容的流形学习算法工具包，有图形示例文件demo.fig，包括laplacian特征映射算法、流形规则调整、svm分类算法等内容，希望对研究机器学习的朋友有用。-contains a rich content of the manifold learning algorithm tool kit, graphics documents demo.fig example, laplacian features include mapping algorithm manifold rul
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基于不断学习的贝叶斯-KNN文本分类算法的设计与实现,给出原始几个类别的文本文件，通过机器学习，获取各个类别文本内容的主要特征，在这个基础上，给出待分类的文件库，系统通过自动分类，对文件库中的文本进行分类，把文件分配到最有可能的类别中。-based learning Bayesian-KNN text classification algorithm design and implementation given several types of the original text file,
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自动地分类和聚集文档，可以选择作为一个Web服务。这个程序完全是用Java编写的，利用了 Weka机器学习工具包。-automatic classification and gathering documents, we can choose as a Web service. This procedure is completely written in Java, Weka use of machine learning kits.
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用朴素贝叶斯算法来进行文件分类,未包含训练数据.-Naive Bayes algorithm is used to carry out classification of documents, not included in training data.
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使用svm实现了分类和拟合功能
带有源文件，包括了java版本和c++版本
-Achieved using the SVM classification and fitting function with the source documents, including a java version and c++ Version
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这些文献包括，分类，聚类，以及多目标优化，可以帮助大家进一步学习和接触此方面的知识，进而进一步发展-These documents include classification, clustering, and multi-objective optimization, which can help us to further study and access to the further development of this knowledge.
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对WORD文档里的二类数据进行分类，使用神经网络的BP算法。采用三层网络。-WORD documents in the two types of data classification, using neural network BP algorithm. Three-tier network.
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Neural network classification and curve fitting
curve fitting including separators and two documents
-Neural network classification and curve fitting
curve fitting including separators and two documents
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这是利用神经网络进行二分类的程序，训练过程采用bp.工程中包含训练文件和测试文件。-This is for two neural network classification procedure, the training process using bp. Project includes training documents
And test files.
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单层竞争神经网络的数据分类—患者癌症发病预测，共3个文件-Single layer neural network data classification in patients with cancer prediction, a total of 3 documents
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利用matlab读取libsvm的工具包，实现数据的自动分类。压缩包里有训练文件及测试文件。-Read libsvm using matlab toolkit, automatic classification of data. Compression bag has training documents and test files.
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