Description: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples. Platform: |
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Description: 机器学习经典书籍The Elements of Statistical Learning--Data Mining, Inference and Prediction. 作者：Friedman-Machine learning classic book The Elements of Statistical Learning Data Mining, Inference and Prediction. Author: Friedman Platform: |
Size: 4515840 |
Author:张中举 |
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Description: 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.
-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 wel Platform: |
Size: 188416 |
Author:xz |
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Description: Information Theory：Inference and Learning Algorithms，信息论参考书-Information Theory: Inference and Learning Algorithms, Information Theory reference Platform: |
Size: 10805248 |
Author:david |
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Description: 信息论推理和学习算法，一本很好的检索信息论和信息检索算法的书籍，值得-Information theory inference and learning algorithms, a good retrieval of information theory and information retrieval algorithms books, it is worth a look Platform: |
Size: 10807296 |
Author:feifei |
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Description: The Elements of Statistical Learning---
Data Mining, Inference, and Prediction，大牛的大作-The Elements of Statistical Learning--- Data Mining, Inference, and Prediction Platform: |
Size: 7526400 |
Author:yujianjun |
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Description: 信息论，推理与学习算法；这个书有课后习题答案的，请相关学习人员参阅。-Information theory, inference and learning algorithms this book after-school Exercise answers, please refer to the relevant study personnel. Platform: |
Size: 8195072 |
Author:gouyabin |
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Description: 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. Platform: |
Size: 6144 |
Author:ruso |
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Description: 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. Platform: |
Size: 4096 |
Author:ruso |
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Description: 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. Platform: |
Size: 6144 |
Author:ruso |
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Description: 《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 publishment with a great value Platform: |
Size: 10975232 |
Author:李若冰 |
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Description: 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.-This is 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. Platform: |
Size: 103424 |
Author:newmerce |
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