The goal of SPID is to provide the user with tools capable to simulate, preprocess, process and classify in vivo and ex vivo MRS signals. These tools are embedded in a matlab graphical user interface (GUI). (Pre)processing and ClaSSification methods can also be automatically run in a row using the matlab command line
標(biāo)簽： preprocess simulate capable provide
上傳時(shí)間： 2014-11-29
上傳用戶：chenbhdt
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
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(biāo)簽： multimedia Semantic analysis research
上傳時(shí)間： 2016-11-24
上傳用戶：蟲蟲蟲蟲蟲蟲
1）解壓縮 2)打開“手寫數(shù)字分類軟件”文件夾，點(diǎn)擊“手寫數(shù)字分類.msi”安裝程序，在安裝向?qū)е羞x擇安裝目錄，安裝完成。 3)打開Matlab軟件程序，將當(dāng)前工作目錄設(shè)為“手寫數(shù)字分類”的安裝目錄，在命令行中輸入“ClaSSification”命令，即可打開手寫數(shù)字分類軟件。 4）內(nèi)含多種識(shí)別方法
上傳時(shí)間： 2014-01-19
上傳用戶：xfbs821
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
Wavelet Subband coding for speaker recognition The fn will calculated subband energes as given in the att tech paper of ruhi sarikaya and others. the fn also calculates the DCT part. using this fn and other algo for pattern ClaSSification(VQ,GMM) speaker identification could be achived. the progress in extraction is also indicated by progress bar.
標(biāo)簽： recognition calculated Wavelet Subband
上傳時(shí)間： 2013-12-08
上傳用戶：guanliya
模式識(shí)別作業(yè)，用近鄰函數(shù)法分類的matlab程序，ClaSSification using matlab
標(biāo)簽： 模式識(shí)別
上傳時(shí)間： 2014-09-11
上傳用戶：lindor
四種聚類算法源代碼及示例代碼,本程序的最終目的是形成一套標(biāo)準(zhǔn)的用于聚類、可擴(kuò)展的工具。包括的內(nèi)容有1. 聚類算法：Kmeans和Kmedoid算法、FCMclust, GKclust, GGclust算法 2. 評(píng)估分類原型：程序可以在二維圖像上繪制出聚類的結(jié)果 3. 驗(yàn)證：程序給每一個(gè)算法提供驗(yàn)證機(jī)制，每個(gè)聚類算法會(huì)統(tǒng)計(jì)Partition Coefficient (PC), ClaSSification Entropy (CE), Partition Index (SC), Separation Index (S), Xie and Beni s Index (XB), Dunn s Index (DI) and Alternative Dunn Index (DII)幾種衡量指標(biāo)。
標(biāo)簽： FCMclust GKclust GGclust Kmedoid
上傳時(shí)間： 2013-12-17
上傳用戶：13160677563
Neural Networks at your Fingertips.rar =============== Network: Adaline Network =============== Application: Pattern Recognition ClaSSification of Digits 0-9 Author: Karsten Kutza Date: 15.4.96 Reference: B. Widrow, M.E. Hoff Adaptive Switching Circuits 1960 IRE WESCON Convention Record, IRE, New York, NY, pp. 96-104, 1960
標(biāo)簽： Network Fingertips Networks Adaline
上傳時(shí)間： 2014-12-22
上傳用戶：lizhizheng88
程序包包含的驗(yàn)證方法，會(huì)根據(jù)以下的多個(gè)指數(shù)分配系數(shù)(Partition Coefficient)，分類熵ClaSSification Entropy ，分區(qū)索引，分離指數(shù)（Separation Index），Xie and Beni s的索引，嚴(yán)重聚類的算法
上傳時(shí)間： 2017-02-21
上傳用戶：playboys0
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