Musical SOM Training
標(biāo)簽： Training Musical SOM
上傳時間： 2013-12-15
上傳用戶：mpquest
Self Organizing Hierarchical Routing
標(biāo)簽： Hierarchical Organizing Routing Self
上傳時間： 2015-11-12
上傳用戶：253189838
Introduction to neural networks, Back Propagation networks, Recurrent networks, Self Oganising networks, Reinforcement learning. Robot Control, Vision systems. Hardware and software Implementaionts.
標(biāo)簽： networks Introduction Propagation Oganising
上傳時間： 2014-01-14
上傳用戶：xcy122677
A self-designed control of PC parallel port communication is introduced in this paper.Taking advantage of the MFC ActiveX technology, it is developed in Visual C++ and offset the scarcity of the control of PC parellel paort communication. An example of frequency measurement shows the detail procedure and application of the control.
標(biāo)簽： self-designed communication introduced parallel
上傳時間： 2015-12-02
上傳用戶：Miyuki
小弟撰寫的類神經(jīng)pca對圖片的壓縮與解壓縮,對來源圖片training過後,可使用該張圖像的特性(eigenvalue和eigenvetex)來對別張圖解壓縮,非常有趣的方式,再設(shè)定threashold時注意時值不要過大,因?yàn)檫@牽涉inverse matrex的計算.
標(biāo)簽： eigenvalue eigenvetex threashol training
上傳時間： 2015-12-02
上傳用戶：wpwpwlxwlx
類神經(jīng)網(wǎng)路的基本運(yùn)算-TLU,為所有學(xué)習(xí)類神經(jīng)入門的的第一個演算法,單一的neural做簡易的training,雖無法解xor的問題,但卻是人類史上的類神經(jīng)的第一步.
標(biāo)簽： training neural TLU xor
上傳時間： 2015-12-02
上傳用戶：851197153
Capture CIS training的一些教程，對學(xué)習(xí)CIS有一些幫助。
標(biāo)簽： training Capture CIS 教程
上傳時間： 2013-12-23
上傳用戶：330402686
SVMcfg: Learns a weighted context free grammar from examples. Training examples (e.g. for natural language parsing) specify the sentence along with the correct parse tree. The goal is to predict the parse tree of new sentences.
標(biāo)簽： examples e.g. weighted Training
上傳時間： 2014-07-26
上傳用戶：zsjzc
This the implementation of structural SVM for training complex alignment models for protein sequence alignment, especially for homology modeling. The structural SVM algorithm can incorporate many relevant features like secondary structure, relative exposed surface area, profiles and their various interaction into the alignment model. It was developed under Linux and compiles under gcc, built upon the svm^light software by Thorsten Joachims.
標(biāo)簽： implementation structural for alignment
上傳時間： 2014-01-11
上傳用戶：chenbhdt
SVMhmm: Learns a hidden Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag sequences for new sentences.
標(biāo)簽： examples e.g. part-of-speech Training
上傳時間： 2015-12-05
上傳用戶：gyq
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