'learning_rate: The learning rate shrinks the contribution of each tree by the specified factor. A lower learning rate means that more trees are needed to model the data, which increases the model's complexity and can lead to overfitting. 'max_depth: The maximum depth of the individual trees...
I. Williams的专著Gaussian Processes for Machine Learning。 PAC(Probably Approximately Correct)可学习性:机器学习中数学分析的一种框架,由图灵奖得主Leslie Valiant于1984年提出。Shai Shalev-Shwartz和Shai Ben-David于2014年的新著Understanding Machine Learning: From Theory To Algorithms值得推荐。 VC(Vapnik–...
1.4 Principal Components Analysis 1.4.1 算法原理 主成分分析(Principal Components Analysis -- PCA)与线性判别分析(Linear Discriminant Analysis -- LDA)有着非常近似的意思,LDA的输入数据是带标签的,而PCA的输入数据是不带标签的,所以PCA是一种unsupervised learning。LDA通常来说是作为一个独立的算法存在,给定了...
Coach- Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms garage- 可重复强化学习研究的工具包 metaworld- An open source robotics benchmark for meta- and multi-task reinforcement learning acme- An Open Source Distributed ...
《Building a Production Machine Learning Infrastructure》 介绍:前Google广告系统工程师Josh Wills 讲述工业界和学术界机器学习的异同,大实话 《Deep Learning Sentiment Analysis for Movie Reviews using Neo4j》 介绍:使用Neo4j 做电影评论的情感分析。 《DeepLearning.University – An Annotated Deep Learning Bibli...
Important foundation principles for all machine learning algorithms, including: The statistical and computer science terms used to describe data, and what they all mean (with pictures). The fundamental problem that all machine learning algorithms solve and why it’s important. The breakdown of algorit...
Machine learning has been hailed as a boon for the new era of data-rich biology for some time now[18–20]. In supervised learning, a set of input attributes are used to predict the value of a target. Machine learning algorithms based on linear models, such as regression, have been e...
John LangfordSebastien BubeckNeural Information Processing Systems
LDA(Linear discriminative analysis) 多分类问题和类别不平衡问题处理0.5h 决策树,GBDT & Random Forest ++ 神经网络 贝叶斯分类器 20 Questions 基础概念 机器学习是研究 关于"学习算法"的学问。 dataset, sample, feature, feature value, feature space, feature vector(一个示例) ...
2.5Generative Learning algorithms 29 2.5.1Gaussian discriminant analysis ( GDA ) 29 2.5.2朴素贝叶斯 ( Naive Bayes ) 34 2.5.3Laplace smoothing 37 2.6Support Vector Machines 37 2.6.1Introduction 37 2.6.2由逻辑回归引出SVM 38 2.6.3function and geometric margin 40 ...