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

First Page		Document Content
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

	Robust Facial Expressions Recognition Using 3D Average Face and Ameliorated AdaBoost Jinhui Chen Yasuo Ariki DocID: 1vqLv - View Document
	NAMBURETE ET AL.: NAKAGAMI-BASED DETECTION IN FETAL NEUROSONOGRAMS Annals of the BMVA Vol. 2013, No. 2, pp 1–Nakagami-Based AdaBoost Learning DocID: 1uGHK - View Document
	Package ‘adabag’ October 14, 2015 Type Package Title Applies Multiclass AdaBoost.M1, SAMME and Bagging Version 4.1 Date DocID: 1rNZm - View Document
	Mittagsseminar Natural Gradient in Evolution Strategies Sebastian U. Stich ETH Z¨ urich DocID: 1rrZp - View Document
	On-line Multi-label Classification A Problem Transformation Approach Jesse Read Supervisors: Bernhard Pfahringer, Geoff Holmes DocID: 1ragU - View Document