I developed an algorithm for using local ICA in denoising multidimensional data. It uses delay embedded version of the data, CLUSTERING and ICA for the separation between data and noise.
標(biāo)簽： multidimensional developed algorithm denoising
上傳時(shí)間： 2016-06-01
上傳用戶：cc1915
An Efficient and Effective Detailed Placement Algorithm Global Swap To identify a pair of cells that can be swapped to reduce wirelength (others are fixed). 2. Vertical Swap Swap a cell with a nearby cell in the segment above or below. 3. Local Re-ordering Re-order consecutive cells locally to reduce wirelength. 4. Single-Segment CLUSTERING Place cells optimally within a segment.
標(biāo)簽： Algorithm Efficient Effective Placement
上傳時(shí)間： 2013-12-18
上傳用戶：ukuk
Recent advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence CLUSTERING and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique’s potential.
標(biāo)簽： experimental generation advances enormous
上傳時(shí)間： 2016-10-23
上傳用戶：wkchong
Many of the pattern fi nding algorithms such as decision tree, classifi cation rules and CLUSTERING techniques that are frequently used in data mining have been developed in machine learning research community. Frequent pattern and association rule mining is one of the few excep- tions to this tradition. The introduction of this technique boosted data mining research and its impact is tremendous. The algorithm is quite simple and easy to implement. Experimenting with Apriori-like algorithm is the fi rst thing that data miners try to do.
標(biāo)簽： 64257 algorithms decision pattern
上傳時(shí)間： 2014-01-12
上傳用戶：wangdean1101
function [U,V,num_it]=fcm(U0,X) ％ MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J. ％ Hathaway on June 21, 1994.) The fuzzification constant ％ m = 2, and the stopping criterion for successive partitions is epsilon =??????. ％*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug******** ％ Purpose:The function fcm attempts to find a useful CLUSTERING of the ％ objects represented by the object data in X using the initial partition in U0.
標(biāo)簽： fcm function Version Routine
上傳時(shí)間： 2014-11-30
上傳用戶：二驅(qū)蚊器
一個(gè)自然語(yǔ)言處理的Java開源工具包。LingPipe目前已有很豐富的功能，包括主題分類（Top Classification）、命名實(shí)體識(shí)別（Named Entity Recognition）、詞性標(biāo)注（Part-of Speech Tagging）、句題檢測(cè)（Sentence Detection）、查詢拼寫檢查（Query Spell Checking）、興趣短語(yǔ)檢測(cè)（Interseting Phrase Detection）、聚類（CLUSTERING）、字符語(yǔ)言建模（Character Language Modeling）、醫(yī)學(xué)文獻(xiàn)下載/解析/索引（MEDLINE Download, Parsing and Indexing）、數(shù)據(jù)庫(kù)文本挖掘（Database Text Mining）、中文分詞（Chinese Word Segmentation）、情感分析（Sentiment Analysis）、語(yǔ)言辨別（Language Identification）等API。
標(biāo)簽： LingPipe Java 自然語(yǔ)言處理 開源
上傳時(shí)間： 2013-12-04
上傳用戶：15071087253
state of art language modeling methods: An Empirical Study of Smoothing Techniques for Language Modeling.pdf BLEU, a Method for Automatic Evaluation of Machine Translation.pdf Class-based n-gram models of natural language.pdf Distributed Language Modeling for N-best List Re-ranking.pdf Distributed Word CLUSTERING for Large Scale Class-Based Language Modeling in.pdf
標(biāo)簽： Techniques Empirical Smoothing Language
上傳時(shí)間： 2016-12-26
上傳用戶：zhuoying119
Quartz is a full-featured, open source job scheduling system that can be integrated with, or used along side virtually any J2EE or J2SE application - from the smallest stand-alone application to the largest e-commerce system. Quartz can be used to create simple or complex schedules for executing tens, hundreds, or even tens-of-thousands of jobs jobs whose tasks are defined as standard Java components or EJBs. The Quartz Scheduler includes many enterprise-class features, such as JTA transactions and CLUSTERING.
標(biāo)簽： full-featured integrated scheduling Quartz
上傳時(shí)間： 2014-01-07
上傳用戶：龍飛艇
統(tǒng)計(jì)模式識(shí)別工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，F(xiàn)eature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and CLUSTERING 4，Support Vector and other Kernel Machines
標(biāo)簽： Statistical Recognition Pattern Toolbox
上傳時(shí)間： 2014-01-03
上傳用戶：璇珠官人
不同于k均值聚類的k中心聚類，2007年SCIENCE文章CLUSTERING by Passing Messages Between Data Points 中的方法
上傳時(shí)間： 2017-07-27
上傳用戶：stewart·
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