本文以此為背景,提出了基于事件驅(qū)動的BDI agent實(shí)現(xiàn)體系結(jié)構(gòu),用信念(Belief)、事件(Event)、規(guī)劃(Plan)等內(nèi)部特征來描述軟件agent,并給出了面向?qū)ο髮哟紊系能浖gent的UML模型,該模型定義了構(gòu)成軟件agent的四個對象:Agent、BeliefSet、Event、Plan 為描述這些對象及其交互關(guān)系,本文以java語言為基礎(chǔ),擴(kuò)展出了能描述這四個對象的java類以及描述其交互關(guān)系
標(biāo)簽： agent Belief Event Plan
上傳時間： 2014-01-09
上傳用戶：tedo811
Coarsening approximations of Belief functions
標(biāo)簽： approximations Coarsening functions Belief
上傳時間： 2014-12-02
上傳用戶：葉山豪
Gaussian Belief propagation code in matlab.
標(biāo)簽： propagation Gaussian Belief matlab
上傳時間： 2017-02-03
上傳用戶：chfanjiang
A Belief function distance metric for orderable sets, Information Fusion 14 (4) (2013) 361–373. 期刊論文
上傳時間： 2017-10-12
上傳用戶：ziyoudexiaod
This publication represents the largest LTC commitmentto an application note to date. No other application noteabsorbed as much effort, took so long or cost so much.This level of activity is justified by our Belief that high speedmonolithic amplifiers greatly interest users.
標(biāo)簽： 高速放大器
上傳時間： 2014-01-07
上傳用戶：wfl_yy
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a Belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
標(biāo)簽： instantaneous algorithm Bayesian Gaussian
上傳時間： 2013-12-19
上傳用戶：jjj0202
The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
標(biāo)簽： Matlab-interfaces inference interface the
上傳時間： 2016-08-27
上傳用戶：gxrui1991
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, Belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
標(biāo)簽： foundations The consists sections
上傳時間： 2017-06-22
上傳用戶：lps11188
ieee754的標(biāo)準(zhǔn)，原英文版的！Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the Belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now.
標(biāo)簽： ieee 754 標(biāo)準(zhǔn)
上傳時間： 2017-07-28
上傳用戶：894898248
2.5 Neural Turing Machine - 2.1 Model - .DS_Store 10KB 2.4 RNN Sequence-to-Sequence Model - 2.8 One Shot Deep Learning - 2.7 Deep Transfer Learning Lifelong Learning especially for RL - 2.2 Optimization - 1.4 Speech Recognition Evolution - 1.2 Deep Belief Network(DBN)(Milestone of Deep Learning Eve) - 1.3 ImageNet Evolution（Deep Learning broke out from here） - 2.3 Unsupervised Learning Deep Generative Model - 2.6 Deep Reinforcement Learning
標(biāo)簽： MoldWizard 使用手冊
上傳時間： 2013-05-15
上傳用戶：eeworm
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