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face authentication TRAINing
標(biāo)簽： authentication TRAINing face
上傳時(shí)間： 2014-01-08
上傳用戶：wangzhen1990
《Google Cluster Computing Faculty TRAINing Workshop》講義，對(duì)Google技術(shù)感興趣可看一下
標(biāo)簽： Computing TRAINing Workshop Cluster
上傳時(shí)間： 2014-01-13
上傳用戶：Zxcvbnm
TRAINing and then recognition of a spesified word in MATLAB
標(biāo)簽： recognition spesified TRAINing MATLAB
上傳時(shí)間： 2013-12-06
上傳用戶：cx111111
spnet can be used for TRAINing neural network
標(biāo)簽： TRAINing network neural spnet
上傳時(shí)間： 2013-11-29
上傳用戶：gmh1314
mobile station TRAINing material
標(biāo)簽： material TRAINing station mobile
上傳時(shí)間： 2017-09-09
上傳用戶：gououo
lynda.com 2007年出品的javascript essential TRAINing教學(xué)視頻(Dori Smith主講)的配套源碼。
標(biāo)簽： javascript essential TRAINing lynda
上傳時(shí)間： 2013-11-28
上傳用戶：yuzsu
Allegro? PCB SI 官方培訓(xùn)教程，以15..5版本為例進(jìn)行講解
標(biāo)簽： Allegro-PCB-SI-Foundations-Traini ng-Manual
上傳時(shí)間： 2013-06-02
上傳用戶：dhb717
Methods for designing a maintenance simulation TRAINing system for certain kind of radio are introduced. Fault modeling method is used to establish the fault database. The system sets up some typical failures， follow the prompts trainers can locate the fault source and confirm the type to accomplish corresponding fault maintenance TRAINing. A TRAINing evaluation means is given to examining and evaluating the TRAINing performance. The system intuitively and vividly shows the fault maintenance process， it can not only be used in teaching， but also in daily maintenance TRAINing to efficiently improve the maintenance operation level. Graphical programming language LabVIEW is used to develop the system platform.
標(biāo)簽： 電臺(tái)維修 模擬訓(xùn)練 方法研究 系統(tǒng)設(shè)計(jì)
上傳時(shí)間： 2013-11-19
上傳用戶：3294322651
最新的支持向量機(jī)工具箱，有了它會(huì)很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast TRAINing of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
上傳時(shí)間： 2013-12-16
上傳用戶：亞亞娟娟123
LVQ學(xué)習(xí)矢量化算法源程序 This directory contains code implementing the Learning vector quantization network. Source code may be found in LVQ.CPP. Sample TRAINing data is found in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The LVQ program accepts input consisting of vectors and calculates the LVQ network weights. If a test set is specified, the winning neuron (class) for each neuron is identified and the Euclidean distance between the pattern and each neuron is reported. Output is directed to the screen.
標(biāo)簽： implementing quantization directory Learning
上傳時(shí)間： 2015-05-02
上傳用戶：hewenzhi
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