'Validation set' diaporamas de présentation

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Large-Scale Machine Learning: SVM

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Ch 3. Decision Tree Learning

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Model Selection and Validation

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Model Selection and Validation

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Chapter 5 NEURAL NETWORKS

Chapter 5 NEURAL NETWORKS. by S. Betul Ceran. Outline. Introduction Feed-forward Network Functions Network Training Error Backpropagation Regularization. Introduction. Multi-Layer Perceptron (1).

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med.gen . r u g .

FUNCTIONAL GENOMICS FOR HEALTH Charles H.C.M. Buys Dept. of Medical Genetics, University of Groningen (c.h.c.m.buys@medgen.umcg.nl). med.gen . r u g. International Consortium Completes Human Genome Project All Goals Achieved; New Vision for Genome Research Unveiled

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Decision Trees

Decision Trees. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Another Example Problem. A Decision Tree. Type. Car. SUV. Minivan. -. Doors. Tires. 2. 4. Blackwall. Whitewall. +. -. -. +. Decision Trees. Decision tree representation

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Additive Groves of Regression Trees

Additive Groves of Regression Trees. Daria Sorokina Rich Caruana Mirek Riedewald. Groves of Trees. New regression algorithm Ensemble of regression trees Based on Bagging Additive models Combination of large trees and additive structure Outperforms state-of the-art ensembles

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TDIDT Learning

TDIDT Learning. Decision Tree. Internal nodes ? tests on some property Branches from internal nodes ? values of the associated property Leaf nodes ? classifications An individual is classified by traversing the tree from its root to a leaf. Sample Decision Tree. Decision Tree Learning.

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Implementation In Tree

Implementation In Tree. Stat 6601 November 24, 2004 Bin Hu, Philip Wong, Yu Ye. Data Background. From SPSS Answer Tree program, we use its credit scoring example There are 323 data points The target variable is credit ranking (good [48%], bad [52%]) The four predictor variables are

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03/11/98 Machine Learning

03/11/98 Machine Learning. Administrative Finish this topic The rest of the time is yours Final exam Tuesday, Mar. 17, 2:30-4:20 p.m., here Additional help today after class (me), Thursday 2-4 (SteveW), Friday after class (me), Saturday 10-12 (me) Last time

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CHURN PREDICTION MODEL IN RETAIL BANKING USING FUZZY C-MEANS CLUSTERING

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Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 9: Ways of speeding up the learning and preventing overfitting. Geoffrey Hinton. Five ways to speed up learning. Use an adaptive global learning rate Increase the rate slowly if its not diverging

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CS539: Project 3

CS539: Project 3. Zach Pardos. Math question response data from 592 students. 1,143 math question attributes {correct, incorrect} Average of 200 questions answered per student (lots of missing values). Class: MCAS SCORE {0-29}. Assistments Online Dataset. Assistments Online Dataset.

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Decision Tree Pruning Methods

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Associations Between Heat Shock Proteins B1 Genotypes and RP in ?Grade 2 and ?3

Associations Between Characteristics and RP in ?Grade 2 and ?3 in the whole set by univariate analysis. Associations Between Heat Shock Proteins B1 Genotypes and RP in ?Grade 2 and ?3.

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????. External Validation of Logistic Regression Models by Testing Homogeneity. ?? : 2013 ? 5 ? 24 ????? 10:00-11:00. ???? Nan Lin ( ?? ). ? ???????? 709 ??.

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