（转载）机器学习方法的PPT

 （转载）机器学习方法的PPT   

  


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 一、特征选择
 二、分类方法 
三、决策树
四、人工神经网络与遗传算法
五、支持向量机
六、图论与聚类方法
其它（待补）
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一、特征选择
[PPT]Feature Selection for [b]Classification [/b]
[PPT][b]Feature Selection for Classification M.Dash, H.Liu [/b]
[PPT][b]Classification and Feature Selection [/b]
[PPT]Feature Saliency in Unsupervised Learning
[PPT][b]Feature Selection/Extraction for Classification Problems [/b]
[PPT]Dynamic Integration of Data Mining Methods Using Selection in a [b]... [/b]
[PPT]Data Visualization and [b]Feature Selection: New Algorithms for ... [/b]
[PPT]Robust [b]feature selection by mutual information distributions [/b]
[PPT]Dimensions
[PPT]WEKKEM: a study in Fractal Dimension and Dimensionality Reduction
二、分类方法 
[PPT]Taxonomy [b]Classification [/b]
[PPT]Linear Methods for [b]Classification [/b]
[PPT]Descriptive Statistics
[PPT]Combining Classical Statistics and [b]Data Mining in Tactical ... [/b]
[PPT]Enhanced [b]classification using hyperlinks [/b]
[PPT][b]Classification Algorithms[/b]
[PPT][b]Classification[/b]
[PPT]Reading Report on “The Foundations of Cost-Sensitive Learning [b]...[/b]
[PPT][b]Classification and Prediction (3) [/b]
[PPT]4.3 [b]Classification of Fuzzy Relation [/b]
[PPT]Classification & Data Mining
[PPT]Machine learning for [b]classification [/b]
[PPT]Heuristic Search
[PPT]Comparing [b]Classification Methods[/b]
[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 [b]...[/b]
[PPT]Visualizing and Exploring Data
[PPT]An Integrated Approach to Decision Making under Uncertainty UCLA [b]... [/b]
 
[PPT]Mining Unusual Patterns in Data Streams: Methodologies and [b]...[/b]
[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 [b]... [/b]
[PPT]Bayesian Optimization Algorithm, Decision Graphs, and Occam’s [b]... [/b]
[PPT]Bayesian Inference
[PPT]Text Mining Technique Overview and an Application to Anonymous [b]...[/b]
[PPT]Improving Text [b]Classification Accuracy by Augmenting Labeled ... [/b]
[PPT]Text Mining Technique Overview and an Application to Anonymous [b]... [/b]
[PPT]Fast and accurate text classification
[PPT]On feature distributional clustering for text categorization
[PPT]Hierarchical [b]Classification of Documents with Error Control [/b]
[PPT]A Study of Smoothing Methods for Language Models Applied to [b]... [/b]
  
三、决策树
[PPT]Decision Trees
[PPT][b]Decision Tree Classification[/b]
[PPT]Induction and Decision Trees
[PPT]AN INTRODUCTION TO DECISION TREES
[PPT][b]Decision Tree Construction [/b]
[PPT][b]Decision Tree Learning II [/b]
[PPT][b]Decision Tree Learning [/b]
[PPT]Decision trees and Rule-Based systems
[PPT]Learning with Identification Trees
[PPT][b]Decision Tree Post-Prunning Methods [/b]
[PPT]Decision Trees that Maximise Margins
[PPT]Introduction to Noise Handling in [b]Decision Tree Induction [/b]
[PPT]A Fuzzy [b]Decision Tree Induction Method for Fuzzy Data [/b]
[PPT]Fuzzy [b]decision tree for continuous classification [/b]
[PPT]Artificial Intelligence Machine Learning I – [b]Decision Tree ... [/b]
[PPT]OCToo: A [b]Decision Tree Program [/b]
 [PPT]Packet [b]Classification using Hierarchical Intelligent Cuttings[/b]
[PPT]Rule Induction Using 1-R and ID3
[PPT]Inferring Rudimentary Rules
[PPT]Deriving [b]Classification Rules [/b]
 
四、人工神经网络与遗传算法
[PPT]Neural Networks
[PPT]Artificial [b]Neural Networks [/b]
[PPT][b]Neural Networks: An Introduction and Overview [/b]
[PPT]Evolving Multiple [b]Neural Networks [/b]
[PPT]Introduction to [b]Neural Networks [/b]
[PPT]Training and Testing [b]Neural Networks [/b]
[PPT]Neuro-Fuzzy and Soft Computing
 [PPT]A Comparison of a Self-Organizing [b]Neural Network Vs. Traditional ...[/b]
[PPT]Breast Cancer Diagnosis via Neural Network Classification
[PPT]Effective Data Mining Using Neural Networks
[PPT]Machine learning and Neural Networks
[PPT]Artificial [b]Neural Networks in Image Analysis [/b]
[PPT][b]Neural Miner [/b]
[PPT]Minimal [b]Neural Networks [/b]
[PPT]Learning with Perceptrons and [b]Neural Networks [/b]
[PPT]Feature Selection for Intrusion Detection Using SVMs and ANNs
[PPT]Artificial [b]Neural Networks: Supervised Models [/b]
[PPT]Optimal linear combinations of [b]Neural Networks [/b]
[PPT]Artificial [b]Neural Networks for Supervised Learning in Data Mining [/b]

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[PPT][b]Neural Computing [/b]
[PPT]Using [b]Neural Networks for Clustering on RSI data and Related ... [/b]
[PPT]Classification and diagnostic prediction using artificial [b]neural ... [/b]
[PPT]Continuous Hopfield network
[PPT]SURVEY ON ARTIFICIAL IMMUNE SYSTEM
 
[PPT]Data Mining with [b]Neural Networks and Genetic Algorithms [/b]
[PPT]Fuzzy Systems, [b]Neural Networks and Genetic Algorithms [/b]
[PPT]Evolving Multiple Neural Networks
[PPT]Genetic Algorithms
[PPT]Multi-objective Optimization Using Genetic Algorithms. [b]...[/b]
[PPT]Performance of Genetic Algorithms for Data [b]Classification [/b]
[PPT]Evolutionary Algorithms
[PPT]Basic clustering concepts and clustering using Genetic Algorithm
 
五、支持向量机
[PPT][b]Support Vector Machine[/b]
[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 [b]Concept Analysis [/b]
[PDF][b]Clustering Through Decision Tree Construction [/b]
[PPT]Concept Learning II
[PPT]Minimum Partitioning and Clustering Algorithms
[PPT]5. Partitioning
[PPT]Constrained [b]Graph Clustering [/b]
[PPT]Bi-clustering and co-similarity of documents and words using [b]... [/b]
[PPT]Biclustering of Expressoin Data
[PPT][b]Classification, clustering, similarity [/b]
[PPT]Clustering Using Random Walks
[PPT]Mining [b]Association Rules [/b]
[PPT]An Overview of Clustering Methods
 
[PPT][b]Matching[/b]
[PPT]Faster Subtree Isomorphism
[PPT]Similarity Flooding
[PPT]Entangled [b]Graphs Bipartite correlations in multipartite states [/b]
[PPT]Maximum Planar Subgraphs in Dense [b]Graphs [/b]
[PPT]Matching in bipartite graphs
[PPT]Voting and Consensus Mechanisms
[PPT]Chapter 12 Assignments and Matchings
[PPT]Geometric Constraint Satisfaction Problem Adoption of algebraic [b]... [/b]
[PPT]The Weighted Clique Transversal Set Problem on Distance- [b]... [/b]
[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 – [b]... [/b]
[PPT]Hypergraph
[PPT]ADT [b]Graph [/b]
 
[PPT][Kruksal’s Algorithm]
[PPT]Branch-and-Cut
[PPT]GRAPHS
[PPT]Graphs
[PPT]Trees
[PPT]Trees and Graphs
PPT][b]Graph Algorithms [/b]
[PPT][b]Graph Problems [/b]
[PPT]Shorter Path Algorithms
 [PDF]Trees General Trees A Connected [b]Graph A tree Rooted Trees Rooted ... [/b]
[PPT]Chapter 2 Graphs and Independence
[PPT][b]Graph Algorithms (or, The End Is Near) [/b]
[PPT]Greedy [b]Graphs [/b]
[PPT]Integrating Optimization and Constraint Satisfaction
[PPT]Conceptual [b]Graphs [/b]
[PPT]Guiding Inference with [b]Conceptual Graphs [/b]
[PPT]Graph-Based Concept Learning
[PPT]Graphs and Digraphs
[PPT]The [b]Graph Abstract Data Type [/b]
[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][b]Graph Structures and Algorithms [/b]
[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
 

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