（转载）机器学习方法的PPT

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 Classification

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

[PPT]Classification and Feature Selection

[PPT]Feature Saliency in Unsupervised Learning

[PPT]Feature Selection/Extraction for Classification Problems

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

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

[PPT]Robust feature selection by mutual information distributions

[PPT]Dimensions

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

二、分类方法

[PPT]Taxonomy Classification

[PPT]Linear Methods for Classification

[PPT]Descriptive Statistics

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

[PPT]Enhanced classification using hyperlinks

[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、人工神经网络与遗传算法

[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、支持向量机

[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

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