搜索资源列表
-
0下载：
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.
-
-
0下载：
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.
-
-
0下载：
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.
-
-
1下载：
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.
-
-
0下载：
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
-
-
0下载：
《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
-
-
0下载：
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
-
-
0下载：
code producing figure 1.12 of the book "informatioin theory,inference,and learning algorithm"
-
-
0下载：
exercise 4.11 of the book "informatioin theory,inference,and learning algorithm"
-
-
0下载：
produce figure 4.9 of the book "informatioin theory,inference,and learning algorithm"
-
-
0下载：
exercise4.11 of the book "informatioin theory,inference,and learning algorithm"
-
-
0下载：
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
-
-
2下载：
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
-
-
0下载：
本书从工程应用与实践的角度，对模糊推理与模糊控制系统作了深入浅出的介绍，并以多
个实例详细地介绍了模糊推理的学习及其在 matlab模糊逻辑工具箱中的实现，使得读者可以尽快地掌握模糊逻辑的内容与模糊控制的实现和使用。-Book from the point of view of engineering application and practice of fuzzy reasoning and fuzzy control system was introduced in simple te
-
-
0下载：
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
-
-
0下载：
GPML Matlab Code
The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning. It has since grown to allow more likelihood functions, further inference methods and a flexibl
-
-
0下载：
In mathematics, a relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification.The RVM has an identical functional form to the support vector machine, b
-
-
0下载：
source code of computer vision,models,learning and inference.
-
-
1下载：
MATLAB code. It was originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes for Machine Learning.(The code provided here originally demonstrated the main algorithms from Rasmussen and Williams: Gaussian Processes f
-
-
0下载：
Fast R-CNN是在R-CNN的基础上进行的改进，大致框架是一致的。总体而言，Fast R-CNN相对于R-CNN而言，主要提出了三个改进策略：
1. 提出了RoIPooling，避免了对提取的region proposals进行缩放到224x224，然后经过pre-trained CNN进行检测的步骤，加速了整个网络的learning与inference过程，这个是巨大的改进，并且RoIPooling是可导的，因此使得整个网络可以实现end-to-end learning，这个可以认为是
-
