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.
標簽： Classifiers Implemented Bayesian applying
上傳時間： 2015-09-11
上傳用戶：ommshaggar
Language, Script, and Encoding Identification with String Kernel Classifiers
標簽： Identification Classifiers Language Encoding
上傳時間： 2015-09-29
上傳用戶：nanxia
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
標簽： Introduction Classifiers Algorithms introduces
上傳時間： 2015-10-20
上傳用戶：aeiouetla
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 examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning Classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
向量空間模型分類器 A vector-space model Classifiers package
標簽： vector-space Classifiers package model
上傳時間： 2016-04-10
上傳用戶：wanqunsheng
數據挖掘Classifiers算法，用JAVA實現的分類算法。
標簽： Classifiers 數據挖掘 算法
上傳時間： 2016-05-26
上傳用戶：yepeng139
The matlab code implements the ensemble of decision tree Classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 "
標簽： L. A. Classifiers implements
上傳時間： 2017-08-02
上傳用戶：無聊來刷下
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 algorithms, discretization, lazy learning, bagging, MineSet.
標簽： decision cross-validation estimation bootstrap
上傳時間： 2015-07-26
上傳用戶：趙云興
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 presents a combined approach to semantic adaptation of neural network Classifiers in multimedia framework. It is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a knowledge base. Improved image segmentation results are obtained, which are used for adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content.
標簽： multimedia Semantic analysis research
上傳時間： 2016-11-24
上傳用戶：蟲蟲蟲蟲蟲蟲
This is a case for recognition of hand gestures using the 7 Hu moments and neural network Classifiers
標簽： recognition classifier gestures moments
上傳時間： 2017-08-06
上傳用戶：zhaiye
