'Information gain' diaporamas de présentation

Chapter 3: Supervised Learning

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Chapter 5

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Introduction to Weka

Introduction to Weka. Xingquan (Hill) Zhu Slides copied from Jeffrey Junfeng Pan (UST). Outline. Weka Data Source Feature selection Model building Classifier / Cross Validation Result visualization. WEKA. http://www.cs.waikato.ac.nz/ml/weka/ Data mining software in Java

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Learning I

Learning I. Linda Shapiro CSE 455 . Learning. AI/Vision systems are complex and may have many parameters. It is impractical and often impossible to encode all the knowledge a system needs. Different types of data may require very different parameters.

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Data Mining and Warehousing

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Decision Trees on MapReduce

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CS364 Artificial Intelligence Machine Learning

CS364 Artificial Intelligence Machine Learning. Matthew Casey. Learning Outcomes. Describe methods for acquiring human knowledge Through experience Evaluate which of the acquisition methods would be most appropriate in a given situation Limited data available through example.

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Decision tree

Decision tree. LING 572 Fei Xia 1/10/06. Outline. Basic concepts Main issues Advanced topics. Basic concepts. A classification problem. Classification and estimation problems. Given a finite set of (input) attributes: features Ex: District, House type, Income, Previous customer

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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

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Decision Trees

Decision Trees. Example. Example 2. Examples, which one is better?. Good when. Samples are attribute-value pairs Target function has discrete output values Disjunctions required Missing, noisy training data. Construction. Top down construction

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Genetic Algorithms

Genetic Algorithms. CS121 Spring 2009 Richard Frankel Stanford University. Outline. Motivations/applicable situations What are genetic algorithms? Pros and cons Examples. Motivation.

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Decision Tree Learning

Decision Tree Learning. Presented by Ping Zhang Nov. 26th, 2007. Introduction. Decision tree learning is one of the most widely used and practical method for inductive inference

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Machine Learning: Lecture 9

Machine Learning: Lecture 9. Rule Learning / Inductive Logic Programming / Association Rules. Learning Rules. One of the most expressive and human readable representations for learned hypotheses is sets of production rules ( if-then rules).

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Decision Trees

Decision Trees. What is a decision tree?. Input = assignment of values for given attributes Discrete (often Boolean) or continuous Output = predicated value Discrete - classification Continuous - regression Structure: Internal node - tests one attribute

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Cues to Emotion: Language

Cues to Emotion: Language. Suzanne Yuen Monday Oct 5, 2009 COMS 6998 . Overview. Two-Stream Emotion Recognition for Call Center Monitoring Voice Quality and f 0 Cues for Affect Expression: Implications for Synthesis. Two Stream Emotion Recognition for Call Center Monitoring.

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Testing the Significance of Attribute Interactions

Testing the Significance of Attribute Interactions. Aleks Jakulin & Ivan Bratko Faculty of Computer and Information Science University of Ljubljana Slovenia. Overview. Interactions: The key to understanding many peculiarities in machine learning.

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Developing a Network of Large-Scale Management Experiments in the Pacific Northwest

Southeast Alaska. Southeast Alaska. British Columbia. British Columbia. Washington. Oregon. Washington. Oregon. Clearcut. Control. Small Patches. Retained Overstory. Group Selection. Extended Rotation. Photo courtesy of USDA Forest Service, PNW Research Station. 200 m.

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