'Expected information' diaporamas de présentation

Huffman code and ID3

CS 157 B Lecture 18. Huffman code and ID3. Prof. Sin-Min Lee Department of Computer Science. Data Compression. Data discussed so far have used FIXED length for representation For data transfer (in particular), this method is inefficient.

750 views • 58 slides

Data Mining in Artificial Intelligence: Decision Trees

Data Mining in Artificial Intelligence: Decision Trees. Outline. Introduction: Data Flood Top-Down Decision Tree Construction Choosing the Splitting Attribute Information Gain and Gain Ratio Pruning. Trends leading to Data Flood. More data is generated:

839 views • 56 slides

Algorithms for Distributed Supervised and Unsupervised Learning

Algorithms for Distributed Supervised and Unsupervised Learning. Haimonti Dutta The Center for Computational Learning Systems (CCLS) Columbia University, New York. About ME. BCSE from Jadavpur University, Kolkata MS from Temple University, PA

548 views • 38 slides

SET (6)

SET (6). Prof. Dragomir R. Radev radev@umich.edu. Vector space classification. x2. topic2. topic1. x1. Decision surfaces. x2. topic2. topic1. x1. Decision trees. x2. topic2. topic1. x1. Classification using decision trees. Expected information need

466 views • 34 slides

DECISION TREES

DECISION TREES. An internal node represents a test on an attribute. A branch represents an outcome of the test, e.g., Color=red. A leaf node represents a class label or class label distribution.

199 views • 17 slides

Systematic and Unsystematic Risk

675 views • 5 slides