搜索资源列表
DecisionForest_v3.1
- [1] Quan Wang, Yan Ou, A. Agung Julius, Kim L. Boyer and Min Jun Kim, Tracking Tetrahymena Pyriformis Cells using Decision Trees , 2012 21st International Conference on Pattern Recognition (ICPR), Pages 1843-1847, 11-15 Nov. 2012. [2] Q
Lmethod
- L-method in R statistical language. Implementation of the L-method algorithm for finding the best number of clusters in hierarchical clustering. Algorithm is described here: Salvador, S. and Chan, P., 2004. Determining the number of clusters/seg
Enhancedtextmining
- 强化版本文本挖掘流程，包含分词，分类聚类，分词结果评估等-Enhanced version of the text mining process, including word segmentation, classification clustering, segmentation results uation, etc.
InfoRetri
- 基于朴素贝叶斯的文本分类，包含去停用词，分词，特征提取，分类等-Text classification, based libsvm, included to stop words, segmentation, feature extraction and classification
NLPLibSVM
- libsvm分词训练集的java版本。包括libsvm.jar以及训练集样本-Libsvm version of the Java word segmentation training set. Including libsvm.jar and training set samples
K-meansbased-on-image-segmentation
- 基于K-means聚类算法的图像分割 代码及实验测试结果-K-means clustering algorithm based on image segmentation code and some experimental results
ICTCLAS_api
- 用于为指定文本进行分词操作。按照不同的词性进行分词。-Used to specify the text for the operation of word segmentation. According to different parts of speech.
kmeansClassifier
- 该程序实现了keans分类，使用IK分词技术实现分词。-The program implements the k means classification, the use of IK word segmentation technology to achieve word segmentation.
fenciledebeiyesi
- 中文文本分词系统+基于贝叶斯算法的文本分类源码，用matlab实现。-Chinese word segmentation system+ based on Bayes text classification source code, using matlab implementation.
Sogou-character-porfile
- 介绍人物标签处理的过程，从数据采集，分词，预处理，算法选择以及结果展示方面来介绍相关过程。-This paper introduces the process of character label processing, and introduces the process of data acquisition, word segmentation, preprocessing, algorithm selection and result display.
textclustering-master
- 对于大文本进行挖掘聚类，该方法不考虑文字词语出现的频率信息，考虑上下文语境，将所有的字根据预定义的特征进行词位特征学习，获得一个训练模型。然后对待分字符串的每一个字进行词位标注，最后根据词位定义获得最终的分词结果。(Digging for large text clustering, the method does not consider the text word frequency of information, considering the context, all the words