extreme learning machine 例子 run segment_mean
標(biāo)簽： segment_mean learning extreme machine
上傳時間： 2013-12-05
上傳用戶：笨小孩
extreme learning machine 例子 run satimage_mean
標(biāo)簽： satimage_mean learning extreme machine
上傳時間： 2013-12-21
上傳用戶：kelimu
extreme learning machine例子 run sinc_mean
標(biāo)簽： sinc_mean learning extreme machine
上傳時間： 2013-12-04
上傳用戶：569342831
Auto boot is a service which can reboot a W2K machine at specified time daily.
標(biāo)簽： specified service machine reboot
上傳時間： 2016-06-08
上傳用戶：康郎
list of matlab m-files on matlab 7.0. learning , support vector machine and some utility routines : autocorrelation, linearly scale randomize the row order of a matrix
標(biāo)簽： matlab learning routines m-files
上傳時間： 2017-07-24
上傳用戶：evil
PCA in (learning machine) java.
標(biāo)簽： learning machine java PCA
上傳時間： 2017-09-24
上傳用戶：sunjet
JILRuntime A general purpose, register based virtual machine (VM) that supports object-oriented features, reference counting (auto destruction of data as soon as it is no longer used, no garbage collection), exceptions (handled in C/C++ or virtual machine code) and other debugging features. Objects and functions can be written in virtual machine code, as well as in C or C++, or any other language that can interface to C object code. The VM is written for maximum performance and thus is probably not suitable for embedded systems where a small memory footprint is required. Possible uses of the VM are in game development, scientific research, or to provide a stand-alone, general purpose programming environment.
標(biāo)簽： object-oriented JILRuntime register supports
上傳時間： 2013-12-23
上傳用戶：cc1015285075
mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions. MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36 Methods obtained from various authors. (1) MDS -- Michael Lee (2) ISOMAP -- J. Tenenbaum, de Silva, & Langford (3) LLE -- Sam Roweis & Lawrence Saul (4) Hessian LLE -- D. Donoho & C. Grimes (5) Laplacian -- M. Belkin & P. Niyogi (6) Diffusion Map -- R. Coifman & S. Lafon (7) LTSA -- Zhenyue Zhang & Hongyuan Zha
標(biāo)簽： demonstration Mathematics Department University
上傳時間： 2016-10-29
上傳用戶：youmo81
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
上傳時間： 2016-12-26
上傳用戶：zhuoying119
dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.
標(biāo)簽： probabilistic distributed large-scale dynamical
上傳時間： 2014-01-12
上傳用戶：wangdean1101
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