<--- Back to Details
First PageDocument Content
Computational statistics / Ensemble learning / Pruning / Decision tree learning / Overfitting / Boosting / Ross Quinlan / AdaBoost / Algorithm / Machine learning / Decision trees / Artificial intelligence
Date: 2007-10-16 02:55:08
Computational statistics
Ensemble learning
Pruning
Decision tree learning
Overfitting
Boosting
Ross Quinlan
AdaBoost
Algorithm
Machine learning
Decision trees
Artificial intelligence

Improving Decision Tree Pruning through Automatic Programming Stig-Erland Hansen Roland Olsson

Add to Reading List

Source URL: www.nik.no

Download Document from Source Website

File Size: 96,65 KB

Share Document on Facebook

Similar Documents

Practical Secure Decision Tree Learning in a Teletreatment Application Sebastiaan de Hoogh1 , Berry Schoenmakers2 , Ping Chen3 , and Harm op den Akker4 1

Practical Secure Decision Tree Learning in a Teletreatment Application Sebastiaan de Hoogh1 , Berry Schoenmakers2 , Ping Chen3 , and Harm op den Akker4 1

DocID: 1unpF - View Document

Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning  Yury Ustinovskiy Valentina Fedorova Gleb Gusev Pavel Serdyukov

Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning Yury Ustinovskiy Valentina Fedorova Gleb Gusev Pavel Serdyukov

DocID: 1tqdY - View Document

Decision Tree Learning  1 Decision Trees •  Tree-based classifiers for instances represented as feature-vectors.

Decision Tree Learning 1 Decision Trees •  Tree-based classifiers for instances represented as feature-vectors.

DocID: 1sy0I - View Document

Learning from uncertain data: a decision tree approach Cecília Nunes Anders Jonsson  PhySense Group

Learning from uncertain data: a decision tree approach Cecília Nunes Anders Jonsson PhySense Group

DocID: 1s4pL - View Document