Description: 一个很好的文本分类器，采用朴素贝叶斯分类的方法，并加以改进！-a very good text classifiers using Bayesian classification methods, and improved! Platform: |
Size: 75011 |
Author:lethe |
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Description: 一个很好的文本分类器，采用朴素贝叶斯分类的方法，并加以改进！-a very good text classifiers using Bayesian classification methods, and improved! Platform: |
Size: 3564544 |
Author:lethe |
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Description: Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate real-world applications it is destined to become essential reading.-Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate real-world applications it is destined to become essential reading. Platform: |
Size: 2846720 |
Author:patapee |
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Description: 基于纹理分析笔迹鉴别系统的设计与实现,文中从笔迹图像预处理、特征提取、分类器以及分类器组合等方而展开研究，设计和实现了一个基于文本独立的离线手写体笔迹鉴别系统软件.-Design and Implementation of the writer identification system based on texture analysis, the paper from the handwriting image preprocessing, feature extraction, classifiers, and classifier combination study, design and implementation of a separate text-based Offline Handwriting identification system software. Platform: |
Size: 972800 |
Author:Chris |
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Description: ADABOOST的源码，为了方便大家理解，我修改了原文中的弱分类器最优阈值寻找的部分代码。-ADABOOST the source, in order to facilitate understanding, I modified the text of the weak classifiers optimal threshold looking for some code. Platform: |
Size: 4096 |
Author:shilinc |
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Description: 空间向量模型VSM的c++实现代码，有利于进行文本分类预处理工作-The c++ vector space model VSM implementation code, help for text classification preprocessing Platform: |
Size: 2048 |
Author:蔡明 |
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Description: 这是matlab编写的3个常用机器学习分类器代码。其中包括了： 1）PCA 分类其；2）LDA分类器：3）naive贝叶斯分类器。 3个算法的实现参考了《Introduction to Machine
Learning》。 除了这3个分类算法的实现外，代码里面还包含了用于测试的main.m 主程序和一个实验的简要报告。实验在著名数据集acoustic_train_data 上进行。-This source code includes the implementation of three famous classifiers in machine learning. They are 1) PCA, 2) LDA and 3) Naive Bayesian. The detail theory behind these classifier can be found text book <<Introduction to Machine Learning>>. Besides the implementation of these 3 algorithms, the main.m for testing and a brief testing report is also included. (The testing is on data set acoustic_train_data) Enjoy:) Platform: |
Size: 371712 |
Author:hhj |
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Description: matlab实现文本分类，内含两个分类器，主要采用线性回归和神经网络-Matlab achieve text classification, containing two classifiers, mainly using linear regression and neural network Platform: |
Size: 3072 |
Author:yao |
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Description: 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 proposed to design better feature representations
and models for both. In this paper, we apply methods recently
developed in machine learning–specifically, large-scale algorithms
for learning the features automatically unlabeled
data–and show that they allow us to construct highly effective
classifiers for both detection and recognition to be used in a
high accuracy end-to-end system.-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 proposed to design better feature representations
and models for both. In this paper, we apply methods recently
developed in machine learning–specifically, large-scale algorithms
for learning the features automatically unlabeled
data–and show that they allow us to construct highly effective
classifiers for both detection and recognition to be used in a
high accuracy end-to-end system. Platform: |
Size: 1145856 |
Author:qwmpg |
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Description: In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,[1]:488 and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines.[2] It also finds application in automatic medical diagnosis.[3] Platform: |
Size: 3072 |
Author:sandy126
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Search - text classifiers