当前位置：
首页 资源下载
搜索资源 - learning classifiers
搜索资源列表
-
0下载：
多分类器集成算法，应用前景广泛，本pdf有益于对该算法的了解和学习，共享之。-multiple classifiers integration algorithm, broad application prospects, the benefits of this algorithm pdf understanding and learning, Sharing.
-
-
0下载：
machine learning, accuracy estimation, cross-validation,
bootstrap, ID3, decision trees, decision graphs, naive-bayes,
decision tables, majority, induction algorithms, classifiers,
categorizers, general logic diagrams, instance-based algorit
-
-
0下载：
a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications. -a Java toolkit for tra
-
-
0下载：
BP神经网络分类器
程序有两种运行状态，一个是学习，另外一个是分类。在学习状态下，在Dos命令符下输入bp learn，便开始学习了，学习的结果放在weight.dat中；在工作状态下，在Dos命令符下输入bp work，便开始识别classfyme.dat中的数据了，识别完成后，结果放在results.dat中。在bp运行的任何一种状态下，都不能手工打开Weight.dat、Sample.dat、classfyme.dat、results.dat中的任何一种。~..~-BP neur
-
-
0下载：
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the
-
-
1下载：
On-line AdaBoost分类器，AdaBoost分类器的改进，在线学习更新分类器，多用于目标跟踪-On-line AdaBoost classifier, AdaBoost classifiers to improve online learning update classifier, used for target tracking
-
-
0下载：
Semantic analysis of multimedia content is an on going research
area that has gained a lot of attention over the last few years.
Additionally, machine learning techniques are widely used for multimedia
analysis with great success. This work pre
-
-
1下载：
This package contains Matlab m-files for learning finite Gaussian mixtures from sample data and performing data classification with Mahalanobis distance or Bayesian classifiers. Each class in training set is learned individually with one of the three
-
-
0下载：
Abstract
We present a component-based, trainable system for detecting
frontal and near-frontal views of faces in still gray
images. The system consists of a two-level hierarchy of Support
Vector Machine (SVM) classifiers. On the first level,
-
-
0下载：
这片论文描述了动态物体的特征跟踪，用到了15个框架。拥有很强的适应性和跟踪能力。作为人脸识别，模式识别，动态跟踪的开发人员，有很好的参考价值。用c++编写，如果用OpenCV更好-This paper describes a visual object detection framework that is capable of processing
images extremely rapidly while achieving high detection rates. There ar
-
-
1下载：
对传统的支持向量机分类器的改进,利用了半监督学习的方法，可以极大的提高分类效率。-The traditional support vector machine classifiers is improved with a semi-supervised learning methods, can greatly improve classification efficiency.
-
-
0下载：
介绍模式识别的基本概念，详述了贝叶斯，参数估计，线性分类器，神经网络，随机方法，无监督学习与聚类等-Introduce the basic concepts of pattern recognition, Bayesian detailed, parameter estimation, linear classifiers, neural networks, stochastic methods, unsupervised learning and clustering, etc.
-
-
0下载：
jBNC is a Java toolkit for training, testing, and applying
Bayesian Network Classifiers. Implemented classifiers have been
shown to perform well in a variety of artificial intelligence,
machine learning, and data mining applications.
jBNC
-
-
0下载：
基于自组织数据挖掘的多分类器集成选择的程序-Multiple classifiers ensemble selection based on GMDH
-
-
0下载：
Abstract—In recent years, the use of machine learning
algorithms (classifiers) has proven to be of great value in
solving a variety of problems in software engineering including
software faults prediction. This paper extends the idea of
predi
-
-
2下载：
周志华提出的Tri-Training源代码，利用三个分类器的半监督学习方法，Java版本-Zhou Zhihua proposed by the Tri-Training source code, use semi-supervised learning method, the Java version of the three classifiers
-
-
1下载：
自主学习把稀疏自编码器和分类器实现结合。先通过稀疏自编码对无标签的5-9的手写体进行训练得到最优参数，然后通过前向传播，得到训练集和测试集的特征，通过0-4有标签训练集训练出softmax模型，然后输入测试集到分类模型实现分类。-Independent Learning the encoder and the sparse classifiers achieve the combination. First through sparse coding since no label was ha
-
-
0下载：
用c编译的online svm适合大数据快速学习的分类器，对内存要求较低，适合数据量大的数据。-C compiled with online svm suitable for large data quickly learning classifiers, low memory requirements for large amount of data.
-
-
0下载：
集成学习将若干基分类器的预测结果进行综合，具体包括Bagging算法和AdaBoost算法；还有随机森林算法，利用多棵树对样本进行训练并预测的一种分类器-Integrated learning integrates the prediction results of several base classifiers, including Bagging algorithm and AdaBoost algorithm and random forest algorithm, using a t
-
-
0下载：
Reading text photographs is a challenging
problem that has received a significant amount of attention.
Two key components of most systems are (i) text detection from
images and (ii) character recognition, and many recent methods
have been pro
-
