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SB2_ControlSettings
- 这是相关向量机的工具包文件的part1,相关向量机作为机器学习的一种，被广泛用于数据预测、模式识别等等。-This is related to vector machine toolkit files part1, relevance vector machine as a machine learning, it is widely used for data prediction, pattern recognition, and so on.
SB2_Diagnostic
- 这是相关向量机的工具包文件的part2,相关向量机作为机器学习的一种，被广泛用于数据预测、模式识别等等。-This is related to vector machine toolkit files part2, relevance vector machine as a machine learning, it is widely used for data prediction, pattern recognition, and so on.
SB2_FormatTime
- 这是相关向量机的工具包文件的part3,相关向量机作为机器学习的一种，被广泛用于数据预测、模式识别等等。-This is related to vector machine toolkit files part3, relevance vector machine as a machine learning, it is widely used for data prediction, pattern recognition, and so on.
SB2_FullStatistics
- 这是相关向量机的工具包文件的part4,相关向量机作为机器学习的一种，被广泛用于数据预测、模式识别等等。-This is related to vector machine toolkit files part4, relevance vector machine as a machine learning, it is widely used for data prediction, pattern recognition, and so on.
SB2_Initialisation
- 这是相关向量机的工具包文件的part5,相关向量机作为机器学习的一种，被广泛用于数据预测、模式识别等等。-This is related to vector machine toolkit files part5, relevance vector machine as a machine learning, it is widely used for data prediction, pattern recognition, and so on.
SB2_Likelihoods
- 这是相关向量机的工具包文件的part6,相关向量机作为机器学习的一种，被广泛用于数据预测、模式识别等等。-This is related to vector machine toolkit files part6, relevance vector machine as a machine learning, it is widely used for data prediction, pattern recognition, and so on.
K-proximity-algorithm
- 机器学习K临近算法python代码，整个工程都有，直接可以运行。-Machine learning algorithm K near the python code, the entire project has a direct run.
Ch03
- Python 机器学习与实战 第三章 决策树分类问题-Python Machine Learning and practical decision tree classification Chapter III
mvrvm_matlab
- 多维支持向量机 可以解决小样本情况下的机器学习问题。可以提高泛化性能。可以解决高维问题。可以解决非线性问题。可以避免神经网络结构选择和局部极小点问题。-Multidimensional support vector machine can solve the problem of machine learning small sample case. You can improve the generalization performance. You can solve high-dimens
NN-ML
- 《神经网络与机器学习》加Simon的配套程序-Neural Networks and Learning Machines
machinelearninginaction
- 目前市面上机器学习最为畅销《机器学习实战》书籍上全部源代码-Selling machine learning combat all the source code
lab6
- 在统计计算中，最大期望（EM）算法是在概率（probabilistic）模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐藏变量（Latent Variable）。最大期望经常用在机器学习和计算机视觉的数据聚类（Data Clustering）领域。
machine-learning
- 这本书为机器学习书籍，里面有许多机器学习理论介绍和相关源码练习-This book machine learning books, there are a lot of machine learning theory to practice presentations and Related Items
machine-learning1-5
- 斯坦福大学机器学习课程，2016版的，前五周课后作业习题程序实例，都是验证过的，可以作为学习参考！-Machine learning courses at Stanford University, 2016 edition, the first five weeks of homework exercises instance, are verified, as a study reference!
machinelearninginaction
- 这是书籍机器学习实践的 每一章节的代码，代码用Python写的，很实用。-The machine learns every chapter code
uu333
- 是机器学习的例程，处理信号的时频分析，完整的基于HMM的语音识别系统。- Machine learning routines, When processing a signal frequency analysis, Complete HMM-based speech recognition system.
machine-learning-yearning-andrew-ng
- 机器学习 andrew ng,Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is working on a machine learning a
mechine-learning
- 本书第一部分主要介绍机器学习基础，以及如何利用算法进行分类，并逐步介绍了多种经典的监督学习算法，如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树（CART）算法等。第三部分则重点介绍无监督学习及其一些主要算法：k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具。 全书通过精心编排的实例，切入日常工作任务，摒弃学术化语言，利用高效的可复用Python代码来阐释如何处理统
Machinelearning
- 机器学习官方文档，对机器学习进行详细的介绍。(machinary learning, introduce the machinary learning.)
PA-1-data-text
- 这个编程作业实现和测试的回归方法还介绍了在演讲和问题集(In this programming assignment you will implement and test some of the regression methods that were introduced in the lecture and in the problem sets.)