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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 proba
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Friedman 的The Elements of Statistical Learning (Data Mining, Inference and Prediction)， 附有djvu阅读器-The Elements of Statistical Learning (Data Mining, Inference and Prediction) by Friedman, along with djvu reader
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Infer.NET is a .NET framework for machine learning. It provides state-of-the-art message-passing algorithms and statistical routines for performing Bayesian inference.
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Bayesian mixture of Gaussians. This set of files contains functions for performing inference and learning on a Bayesian Gaussian mixture model. Learning is carried out via the variational expectation maximization algorithm.
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Mixture of linear regressors. The routines contained in this file allow inference and learning of a mixture of linear-Gaussian regression models. Learning is performed by maximizing the data likelihood via the expectation maximization algorithm.
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Linear dynamical system. This set of functions performs inference and learning of a linear Kalman filter model. Inference is carried out via forward-backward smoothing, and learning is accomplished via the expectation maximization algorithm.
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研究了一种应用于机载火控系统的具有自学习功能的故障诊断系统。首先介绍了系统的
总体结构 然后通过分析火控系统的结构建立了层次诊断模型 ,并通过示例对诊断系统中知识的表
达方法、 推理方法等问题做了详细的分析 最后详细描述了系统的自学习机制。应用具有自学习功
能的故障诊断专家系统 ,可实现综合化机载火控系统的智能故障诊断。-A study of airborne fire control system used in self-learning function of fault di
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CRF learning and inference algorithm for scene labeling and classification
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《统计学习基础：数据挖掘、推理与预测》的全彩英文版，人工智能机器学习领域的必读书目，此版本为最新的第三次排版，很有价值-<The elements of statistical learning:Data mining,inference,and prediction>,color edition,the must-own book in the area of AI/Machine Learning,and it s the latest edition of publishmen
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This a reference implementation for the synthetic experiments on lower
linear envelope inference and learning described in
"Max-margin Learning for Lower Linear Envelope Potentials in Binary
Markov Random Fields", Stephen Gould, ICML 2011
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code producing figure 1.12 of the book "informatioin theory,inference,and learning algorithm"
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exercise 4.11 of the book "informatioin theory,inference,and learning algorithm"
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produce figure 4.9 of the book "informatioin theory,inference,and learning algorithm"
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exercise4.11 of the book "informatioin theory,inference,and learning algorithm"
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基于EKF的神经网络自适应在线学习算法，包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform
regularization in sequential learning. We identify three inference
levels within this hierarchy: model selection, parameter estimation, and
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Alchemy: a tool in c++ for markov logic network inference, parameter learning and structure learning
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此工具箱支持推理和学习HMM模型,拥有的算法有离散输出（DHMM），高斯输出（GHMM），或其混合物的高斯输出（mhmm）。-Hidden Markov Model (HMM) Toolbox for Matlab，This toolbox supports inference and learning for HMMs with discrete outputs (dhmm s), Gaussian outputs (ghmm s), or mixtures of Gaussians outp
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Dynamic Bayesian Networks Representation, Inference And Learning - Kevin Patrick Murphy
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Information Theory inference and Learning Alghoritms Book
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source code of computer vision,models,learning and inference.
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