用元胞自动机实现单色10000个空间位置的细菌分裂试验的算法C语言代码...

基于神经网络的文字识别系统,它通过遗传算法实现。-Neural Network Based Character Recognition system, which through genetic algorithm.

svm training example from svm.org

dos 下运行,蚂蚁找食物,图形演示,动态化显示每一只蚂蚁如何找到事物.-dos run, the ants find food, presentation graphics, dynamic display every ants how to find things.

:一书的配套源程序，结合数据挖掘和机器学习的知识,以java语言实现了具有代表性的各类数据挖掘方法.例如:classifier中的ZeroR.OneR.NaiveBayes.DecisionTable.IBK.C45,还有聚类,数据预处理等-: lt; Lt; Data Mining-- Practical Machine Learning Technology and java achieve gt; Gt; A matching the source, combining data mining and machine learning, the knowledge, java language to a representative of the various types of data mining. For example : the classifier ZeroR . OneR.NaiveBayes.DecisionTable.IBK.C45, clustering, data pretreatment

LIBSVM is an integrated software for support vector classification. LIBSVM provides a simple interface where users can easily link it with their own programs.

BP神经网络自适应步长训练算法，采用最小误差法，梯度下降法，自适应调节权值-BP neural network training anaysis, is realized by using error feed back, gradient descent applied updating of synaptic weights

BP人工神经网络算法，用java代码实现，用scilab图像显示，案例是港口吞吐量的预测-BP artificial neural network algorithm, using java code, using scilab image display, case is the throughput of the port prediction

使用遗传算法、模拟退火算法求解设计结构矩阵，并进行对比分析-The use of genetic algorithms, simulated annealing algorithm design structure matrix, and a comparative analysis of

专业介绍svm算法的书籍，适合有高等数学，线性代数，矩阵论，和数里统计基础的读者，他是一本全面介绍svm的书籍，从数学基础到应用，是svm学习者的一本好书。-Introduction SVM algorithm professional books, suitable for advanced mathematics, linear algebra,矩阵论, and several readers in the statistical basis, he is a comprehensive introduction to SVM books, from mathematical foundations to applications, is an SVM learner Good Books.

统计模式识别算法包，包括线性分类算法，SVM，PCA，LDA，EM，k-means分类等多种常用的模式识别算法。-Statistical pattern recognition algorithm package, including a linear classification algorithm, SVM, PCA, LDA, EM, k-means classification and many other commonly used pattern recognition algorithms.