机器学习的有关资料

 

 一、特征选择

ACM/ICPC在线题库集锦:

 二、分类方法

网址:http://acm.uva.es/
简称: uva
全称: Valladolid Programming Contest Site
所在国:西班牙
付给方式:web方式和email方式
表达:恐怕是世界上名誉最大,最古老的在线题库了。收罗了N卷
的主题素材,非常多国家队的权威都是从这里练出来的。标题包括历届
ACM/ICPC分区赛试题、季后赛试题以及众多其他网络朋友本身出的题
目。标题类型相比完美,难度较平均,不过测验数据非常狡猾,
还要平常更新旧的多寡,在其余地点能通过的程序到了uva就大概
心有余而力不足透过。定期有比赛,而且能够运用它的体系主办自个儿的比赛。
独一的败笔是系统太烂,比赛的时候时临时系统崩溃(不过那和参预
的人太多也可能有关)。

三、决策树

网址:http://acm.zju.edu.cn/
简称: zju/zoj
全称: ZJU Online Judge Contests
所在国:中国
交由形式:web形式
评释:如今境内独一八个在线题库。NJU的Settler队
最首要就在此地磨炼,因为不用出国,很有益于。近些日子有
6卷标题了,标题大好些个是原先的ACM/ICPC分区赛试
题和一些哈工大ACM队员本身出的难点。定时有竞赛。

四、人工神经网络与遗传算法

网址:http://acm.timus.ru/
简称: ural
全称: Ural State University Problem Set Archive with Online Judge
System
所在国:俄罗斯
交由情势:web格局和email方式
申明:那也是二个响当当的题库,因为是俄罗丝人办的,标题标数
学味道比较浓。定时有竞技。这里的难题风格和ACM/ICPC不太同样,
主题素材数学野趣浓,有必然难度,比很多标题都以这种须要部分小工夫的,
一经想出去了程序可能唯有几十行。中中原人民共和国的多数搞OI的中学生在这边
做题,这里的主题材料相比符合中学的OIer。

五、补助向量机

网址:http://acm.sgu.ru/
简称: sgu
全称: Saratov State University :: Online Contester
所在国:俄罗斯
交由格局:web情势
表明:贰个比较新的题库,同样因为是俄罗丝人办的,题指标数学
深意很浓。定时有比赛。

六、图论与聚类方法

上述那多少个是相比较相符出席ACM/ICPC的校友练习用的题库,还应该有部分
诸如USACO等题库,基本上正是面向中学生的,这里就不提了。

其它(待补)

主题算法与数据结构普通话索引:

***********************************

Data Structures 基本数据结构
Dictionaries 字典
Priority Queues 堆
Graph Data Structures 图
Set Data Structures 集合
Kd-Trees 线段树
Numerical Problems 数值难点
Solving Linear Equations 线性方程组
Bandwidth Reduction 带宽压缩
Matrix Multiplication 矩阵乘法
Determinants and Permanents 行列式
Constrained and Unconstrained Optimization 最值难题
Linear Programming 线性规划
Random Number Generation 随机数变化
Factoring and Primality Testing 因子分解/质数推断
Arbitrary Precision Arithmetic 高精度总括
Knapsack Problem 托特包难点
Discrete Fourier Transform 离散Fourier变换
Combinatorial Problems 组合难题
Sorting 排序
Searching 查找
Median and Selection 中位数
Generating Permutations 排列生成
Generating Subsets 子集生成
Generating Partitions 划分生成
Generating Graphs 图的变动
Calendrical Calculations 日期
Job Scheduling 工程布置
Satisfiability 可满足性
Graph Problems — polynomial 图论-多项式算法
Connected Components 连通分支
Topological Sorting 拓扑排序
Minimum Spanning Tree 最小生成树
Shortest Path 最短路径
Transitive Closure and Reduction 传递闭包
Matching 匹配
Eulerian Cycle / Chinese Postman Euler回路/中华夏族民共和国邮递路线
Edge and Vertex Connectivity 割边/割点
Network Flow 网络流
Drawing Graphs Nicely 图的描摹
Drawing Trees 树的刻画
Planarity Detection and Embedding 平面性检查实验和松开
Graph Problems — hard 图论-NP问题
Clique 最大团
Independent Set 独立集
Vertex Cover 点覆盖
Traveling Salesman Problem 游历商难题
Hamiltonian Cycle Hamilton回路
Graph Partition 图的撤并
Vertex Coloring 点染色
Edge Coloring 边染色
Graph Isomorphism 同构
Steiner Tree Steiner树
Feedback Edge/Vertex Set 最大无环子图
Computational 吉优metry 总计几何
Convex Hull 凸包
Triangulation 三角剖分
Voronoi Diagrams Voronoi图
Nearest Neighbor Search 近来点对查询
Range Search 范围查询
Point Location 地点查询
Intersection Detection 碰撞测量检验
Bin Packing 装箱难题
Medial-Axis Transformation 中轴调换
Polygon Partitioning 多边形分割
Simplifying Polygons 多边形化简
Shape Similarity 相似多边形
Motion Planning 运动设计
Maintaining Line Arrangements 平面分割
Minkowski Sum Minkowski和
Set and String Problems 集结与串的标题
Set Cover 会集覆盖
Set Packing 集结配置
String Matching 形式相配
Approximate String Matching 模糊相配
Text Compression 压缩
Cryptography 密码
Finite State Machine Minimization 西周自动机简化
Longest Common Substring 最长公共子串
Shortest Common Superstring 最短公共父串

一、特征选拔

书:
算法类:
N. Wirth, Algorithms + Data Structures = Programs, Prentice-Hall,
Englewood Cl
iffs, 1975.

[PPT]Feature Selection for Classification

N. Wirth, Systematic Programming An Introduction, Prentice Hall, 1973.

[PPT]Feature Selection for Classification M.Dash, H.Liu

A. Engel, Exploring mathematics with your computer, The Mathematical
Associati
on of America, 1993.

[PPT]Classification and Feature Selection

H. Papadimitriou, K. Steigltz, Combinatorial optimization – Algorithms
and co
mplexity, Dover, PUBNS, 1998.

[PPT]Feature Saliency in Unsupervised Learning

A. Vitek, I. Tvrda i dr., Problems in programming / experience through
practic
e, John Wiley & Sons Ltd., 1991.

[PPT]Feature Selection/Extraction for Classification Problems

T. H. Cormen, C. E. Leiserson, R. L. Rivest, S. Stein, Introduction to
Algorit
hms, The MIT Press, 2001.

[PPT]Dynamic Integration of Data Mining Methods Using Selection in a

D. E. Knuth, The Art of Computer Programming, 2nd Edition,
Addison-Wesley, Vol
ume 1: Fundamental Algorithms, 1997.; Volume 2: Seminumerical
Algorithms, 1997
.; Volume 3: Sorting and Searching, 1998.

[PPT]Data Visualization and Feature Selection: New Algorithms for …

Z. Michalewicz, D. B. Fogel, How to Solve It: Modern Heuristics,
Springer-Verl
ag Berlin, 1999.

[PPT]Robust feature selection by mutual information distributions

Steven S. Skiena, The Algorithm Design Manual, Springer-Verlag New York,
Ins.,

[PPT]Dimensions

[PPT]WEKKEM: a study in Fractal Dimension and Dimensionality Reduction

A. Shen, Algorithms and Programming – Problems and Solutions, Birkh?user
Bosto
n, 1997.

二、分类方法 

计算机算法导引 机械

[PPT]Taxonomy Classification

赛题解析类:
ACM 试题剖析(一)、(二)、(三) 吴扁担花 北大
ACM 国际大学生程序设计竞技入门 周学斌山(曼谷) 机械出版
整合数学/图论/奥林匹克音信学国内外赛题剖判 吴巴厘虎 王建德
ACM/ICPC 试题深入分析 王建德

[PPT]Linear Methods for Classification

理论类:
M. Sipser, Introduction to Theory of Computation.

[PPT]Descriptive Statistics

H. Lewis & C. Papadimitriou, Elements of the theory of computation.

[PPT]Combining Classical Statistics and Data Mining in Tactical …

J. Hopcroft, R. Motwani & J. Ullman. Introduction to Automata
Theory, Languages, and Computation.

[PPT]Enhanced classification using hyperlinks

初稿链接:http://evilcat.blogchina.com/4785061.html

[PPT]Classification Algorithms

[PPT]Classification

[PPT]Reading Report on “The Foundations of Cost-Sensitive Learning …

[PPT]Classification and Prediction (3)

[PPT]4.3 Classification of Fuzzy Relation

[PPT]Classification & Data Mining

[PPT]Machine learning for classification

[PPT]Heuristic Search

[PPT]Comparing Classification Methods

[PPT]A Practical Algorithm to Find the Best Episode Patterns

[PPT]Taxonomy of Data-Mining/Knowledge Discovery Tasks

[PPT]Mining Frequent Patterns Without Candidate Generation

 [PPT]KNOWLEDGE AND REASONING

[PPT]Comparisons of Capabilities of Data Mining Tools

[PPT]Uncertainty Reduction in Data Mining: A Case study for Robust …

[PPT]Visualizing and Exploring Data

[PPT]An Integrated Approach to Decision Making under Uncertainty UCLA

 

[PPT]Mining Unusual Patterns in Data Streams: Methodologies and …

[PPT]Learning: Nearest Neighbor

[PPT]Structured Principal Component Analysis

[PPT]Machine Learning through Probabilistic Models

[PPT]Advances in Bayesian Learning

[PPT]Using Discretization and Bayesian Inference Network Learning for

[PPT]Bayesian Optimization Algorithm, Decision Graphs, and Occam’s …

[PPT]Bayesian Inference

[PPT]Text Mining Technique Overview and an Application to Anonymous

[PPT]Improving Text Classification Accuracy by Augmenting Labeled …

[PPT]Text Mining Technique Overview and an Application to Anonymous

[PPT]Fast and accurate text classification

[PPT]On feature distributional clustering for text categorization

[PPT]Hierarchical Classification of Documents with Error Control

[PPT]A Study of Smoothing Methods for Language Models Applied to …

  

三、决策树

[PPT]Decision Trees

[PPT]Decision Tree Classification

[PPT]Induction and Decision Trees

[PPT]AN INTRODUCTION TO DECISION TREES

[PPT]Decision Tree Construction

[PPT]Decision Tree Learning II

[PPT]Decision Tree Learning

[PPT]Decision trees and Rule-Based systems

[PPT]Learning with Identification Trees

[PPT]Decision Tree Post-Prunning Methods

[PPT]Decision Trees that Maximise Margins

[PPT]Introduction to Noise Handling in Decision Tree Induction

[PPT]A Fuzzy Decision Tree Induction Method for Fuzzy Data

[PPT]Fuzzy decision tree for continuous classification

[PPT]Artificial Intelligence Machine Learning I – Decision Tree …

[PPT]OCToo: A Decision Tree Program

 [PPT]Packet Classification using Hierarchical Intelligent Cuttings

[PPT]Rule Induction Using 1-R and ID3

[PPT]Inferring Rudimentary Rules

[PPT]Deriving Classification Rules

 

四、人工神经互联网与遗传算法

[PPT]Neural Networks

[PPT]Artificial Neural Networks

[PPT]Neural Networks: An Introduction and Overview

[PPT]Evolving Multiple Neural Networks

[PPT]Introduction to Neural Networks

[PPT]Training and Testing Neural Networks

[PPT]Neuro-Fuzzy and Soft Computing

 [PPT]A Comparison of a Self-Organizing Neural Network Vs. Traditional

[PPT]Breast Cancer Diagnosis via Neural Network Classification

[PPT]Effective Data Mining Using Neural Networks

[PPT]Machine learning and Neural Networks

[PPT]Artificial Neural Networks in Image Analysis

[PPT]Neural Miner

[PPT]Minimal Neural Networks

[PPT]Learning with Perceptrons and Neural Networks

[PPT]Feature Selection for Intrusion Detection Using SVMs and ANNs

[PPT]Artificial Neural Networks: Supervised Models

[PPT]Optimal linear combinations of Neural Networks

[PPT]Artificial Neural Networks for Supervised Learning in Data Mining

[PPT]Neural Computing

[PPT]Using Neural Networks for Clustering on RSI data and Related …

[PPT]Classification and diagnostic prediction using artificial neural

[PPT]Continuous Hopfield network

[PPT]SURVEY ON ARTIFICIAL IMMUNE SYSTEM

 

[PPT]Data Mining with Neural Networks and Genetic Algorithms

[PPT]Fuzzy Systems, Neural Networks and Genetic Algorithms

[PPT]Evolving Multiple Neural Networks

[PPT]Genetic Algorithms

[PPT]Multi-objective Optimization Using Genetic Algorithms. …

[PPT]Performance of Genetic Algorithms for Data Classification

[PPT]Evolutionary Algorithms

[PPT]Basic clustering concepts and clustering using Genetic Algorithm

 

五、协理向量机

[PPT]Support Vector Machine

[PPT]Support Vector Machines ch1. The Learning Methodology

[PPT]Kernel “Machine” Learning

[PPT]Relevance Vector Machine (RVM)

[PPT]Texture Segmentation using Support Vector Machines

[PPT]Large Margin Classifiers and a Medical Diagnostic Application

[PPT]C4.5 and SVM

[PPT]Support Vector Machines Project

[PPT]Scaling multi-class SVMs using inter-class confusion

[PPT]Mathematical Programming in Support Vector Machines

 

六、图论与聚类方法

[PPT]Clustering Algorithms

[PPT]Data Clustering: A Review

[PPT]Identifying Objects Using Cluster and Concept Analysis

[PDF]Clustering Through Decision Tree Construction

[PPT]Concept Learning II

[PPT]Minimum Partitioning and Clustering Algorithms

[PPT]5. Partitioning

[PPT]Constrained Graph Clustering

[PPT]Bi-clustering and co-similarity of documents and words using …

[PPT]Biclustering of Expressoin Data

[PPT]Classification, clustering, similarity

[PPT]Clustering Using Random Walks

[PPT]Mining Association Rules

[PPT]An Overview of Clustering Methods

 

[PPT]Matching

[PPT]Faster Subtree Isomorphism

[PPT]Similarity Flooding

[PPT]Entangled Graphs Bipartite correlations in multipartite states

[PPT]Maximum Planar Subgraphs in Dense Graphs

[PPT]Matching in bipartite graphs

[PPT]Voting and Consensus Mechanisms

[PPT]Chapter 12 Assignments and Matchings

[PPT]Geometric Constraint Satisfaction Problem Adoption of algebraic

[PPT]The Weighted Clique Transversal Set Problem on Distance- …

[PPT]A Better Algorithm for Finding Planar Subgraph

[PPT]HyperCuP

[PPT]The Disjoint Set ADT

[PPT]Trees, Hierarchies, and Multi-Trees Craig Rixford IS 247 – …

[PPT]Hypergraph

[PPT]ADT Graph

 

[PPT][Kruksal’s Algorithm]

[PPT]Branch-and-Cut

[PPT]GRAPHS

[PPT]Graphs

[PPT]Trees

[PPT]Trees and Graphs

PPT]Graph Algorithms

[PPT]Graph Problems

[PPT]Shorter Path Algorithms

 [PDF]Trees General Trees A Connected Graph A tree Rooted Trees Rooted

[PPT]Chapter 2 Graphs and Independence

[PPT]Graph Algorithms (or, The End Is Near)

[PPT]Greedy Graphs

[PPT]Integrating Optimization and Constraint Satisfaction

[PPT]Conceptual Graphs

[PPT]Guiding Inference with Conceptual Graphs

[PPT]Graph-Based Concept Learning

[PPT]Graphs and Digraphs

[PPT]The Graph Abstract Data Type

[PPT]The ERA Data Model: Entities, Relations and Attributes

[PPT]Stack and Queue Layouts of Directed Acyclic Graphs: Part I

[PPT]Minimum Cost Spanning Trees

[PPT]Chapter 13. Redundancy Elimination

[PPT]Graph Structures and Algorithms

[PPT]Hamiltonian Graphs

[PPT]Hamiltonian Cycles and paths

[PPT]Multilevel Algorithms

[PPT]Greedy and Randomized Local Search

[PPT]Network Capabilities

[PPT]Petri Nets ee249 Fall 2000

[PPT]Petri Nets

[PPT]Extracting hidden information from knowledge networks

[PPT]Interconnect Verification 1

[PPT]Network Flow Approach

[PPT]Statistical Inference, Multiple Comparisons, Random Field Theory

[PPT]Computational Geometry