1、引例 2、贝叶斯概率基础 3、贝叶斯网络概述 4、贝叶斯网络的预测、诊断和训练 4.1 贝叶斯网络的预测 4.2 贝叶斯网络的诊断 4.3 贝叶斯网络的训练

1 人工神经网络发展概况 2 神经网络基本概念 3 前馈神经网络 4 其他神经网络简介 5 神经网络模式识别

了解常用的性能度量 理解常用的比较检验 理解经验误差与过拟合 掌握偏差与方差 灵活运用评估方法

①事件e的各个选择符被放入PS(partial star)中,将ps中的元素按 照各种标准排序 ②在ps中保留最优的m个选择符. ③对ps中的选择符进行完备性和一致性检查,从ps中取出完备一致的描述放入 SOLUTION表中,若 SOLUTION表的大小大于参数

定义1(扩张矩阵)：已知e += 及反例矩阵NE. 对每一 j∈N, 用“死元素”*对 在NE中第j列的所有出现做代换，这 样得出的矩阵叫做正例e +在反例NE背景下的扩张矩阵。记为 EM(e+ |NE), 或简记为EM(e+ )

1 Introduction 2 Learning to Hash Isotropic Hashing Supervised Hashing with Latent Factor Models Supervised Multimodal Hashing with SCM Multiple-Bit Quantization 3 Distributed Learning Coupled Group Lasso for Web-Scale CTR Prediction Distributed Power-Law Graph Computing 4 Stochastic Learning Distributed Stochastic ADMM

定义1(扩张矩阵)：已知e += 及反例矩阵NE. 对每一 j∈N, 用“死元素”*对 在NE中第j列的所有出现做代换，这 样得出的矩阵叫做正例e +在反例NE背景下的扩张矩阵。记为 EM(e+ |NE), 或简记为EM(e+ )

Goal Concept: A concept definition describing the concept to be learned. (It is assumed that this concept definition fails to satisfy the Operationality Criterion. Training Example: An example of the goal concept Domain Theory: A set of rules and facts to be used in explaining how the training example is an example of the goal concept

Reduced-Error Pruning Split data into training and validation set Do until further pruning is harmful: 1. Evaluate impact on validation set of pruning each possible node (plus those below it) 2. Greedily remove the one that most improves validation set accuracy produces smallest version of most accurate subtree · What if data is limited?

Goal Concept: A concept definition describing the concept to be learned. (It is assumed that this concept definition fails to satisfy the Operationality Criterion.) Training Example: An example of the goal concept. Domain Theory: A set of rules and facts to be used in explaining how the training example is an example of the goal concept