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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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Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
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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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在matlab开发环境下 对贝叶斯网络结构进行学习 推理 计算分类，并且对它进行性能分析和比较-Matlab development environment for learning Bayesian network structure inference to calculate the classification, and its performance analysis and comparison
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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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source code of computer vision,models,learning and inference.
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