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Decoding

Decoding

Decoding. Bhiksha Raj and Rita Singh. Recap and Lookahead. Covered so far: String Matching based Recognition Introduction to HMMs Recognizing Isolated Words Learning word models from continuous recordings Building word models from phoneme models

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1.98k views • 175 slides
Decoding Part II

Decoding Part II

Decoding Part II. Bhiksha Raj and Rita Singh. Recap and Lookahead. Covered so far: String Matching based Recognition Introduction to HMMs Recognizing Isolated Words Learning word models from continuous recordings Building word models from phoneme models

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593 views • 59 slides
Data Mining: A Closer Look

Data Mining: A Closer Look

Data Mining: A Closer Look. Chapter 2. 2.1 Data Mining Strategies (p35). Moh!. Classification. Learning is supervised. The dependent variable is categorical. Well-defined classes. Current rather than future behavior. Estimation. Learning is supervised.

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570 views • 42 slides
Training Tied-State Models

Training Tied-State Models

Training Tied-State Models. Rita Singh and Bhiksha Raj. Recap and Lookahead. Covered so far: String Matching based Recognition Introduction to HMMs Recognizing Isolated Words Learning word models from continuous recordings Building word models from phoneme models

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448 views • 30 slides
Training Tied-State Models

Training Tied-State Models

Training Tied-State Models. Rita Singh and Bhiksha Raj. Recap and Lookahead. Covered so far: String Matching based Recognition Introduction to HMMs Recognizing Isolated Words Learning word models from continuous recordings Building word models from phoneme models

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306 views • 30 slides
Data Mining Concepts

Data Mining Concepts

IBM . Data Mining Concepts. Introduction to Directed Data Mining: Decision Trees. Decision Trees. A decision tree is a structure that can be used to divide a large collection of records into successively smaller sets of records by applying a sequence of simple decisions rules.

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411 views • 28 slides
Iterative Dichotomiser 3 (ID3) Algorithm

Iterative Dichotomiser 3 (ID3) Algorithm

Iterative Dichotomiser 3 (ID3) Algorithm Medha Pradhan CS 157B, Spring 2007 Agenda Basics of Decision Tree Introduction to ID3 Entropy and Information Gain Two Examples Basics What is a decision tree?

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1.44k views • 22 slides
Iterative Dichotomiser 3 (ID3) Algorithm

Iterative Dichotomiser 3 (ID3) Algorithm

Iterative Dichotomiser 3 (ID3) Algorithm. Medha Pradhan CS 157B, Spring 2007. Agenda. Basics of Decision Tree Introduction to ID3 Entropy and Information Gain Two Examples. Basics. What is a decision tree?

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392 views • 22 slides
Basics of Decision Trees

Basics of Decision Trees

Basics of Decision Trees . A flow-chart-like hierarchical tree structure Often restricted to a binary structure Root : represents the entire dataset A node without children is called a leaf node . Otherwise is called an internal node . Internal nodes: denote a test on an attribute

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297 views • 18 slides
ID3 and Decision tree

ID3 and Decision tree

ID3 and Decision tree. by Tuan Nguyen. ID3 and Decision tree. ID3 algorithm. Is the algorithm to construct a decision tree Using Entropy to generate the information gain The best value then be selected. ID3 and Decision tree. Entropy. The complete formula for entropy is:

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500 views • 13 slides
ID3 and Decision tree

ID3 and Decision tree

ID3 and Decision tree. by Tuan Nguyen. ID3 and Decision tree. ID3 algorithm. Is the algorithm to construct a decision tree Using Entropy to generate the information gain The best value then be selected. ID3 and Decision tree. Entropy. The complete formula for entropy is:

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764 views • 13 slides

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