✦ LIBER ✦

Regularized Greedy Algorithms for Network Training with Data Noise

✍ Scribed by M. Burger; A. Hofinger

Book ID: 106202549
Publisher: Springer Vienna
Year: 2004
Tongue: English
Weight: 580 KB
Volume: 74
Category: Article
ISSN: 0010-485X
DOI: 10.1007/s00607-004-0081-3

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Training Algorithm with Incomplete Data

Training Algorithm with Incomplete Data for Feed-Forward Neural Networks

✍ Song-Yee Yoon; Soo-Young Lee 📂 Article 📅 1999 🏛 Springer US 🌐 English ⚖ 58 KB

Classification algorithm sensitivity to

Classification algorithm sensitivity to training data with non representative attribute noise

✍ Michael Mannino; Yanjuan Yang; Young Ryu 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 865 KB

Regularization as a substitute for pre-p

Regularization as a substitute for pre-processing of data in neural network training

✍ J. Sjöberg 📂 Article 📅 1992 🏛 Elsevier Science ⚖ 379 KB

Training neural networks with harmony se

Training neural networks with harmony search algorithms for classification problems

✍ Sinem Kulluk; Lale Ozbakir; Adil Baykasoglu 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 413 KB

Comparing backpropagation with a genetic

Comparing backpropagation with a genetic algorithm for neural network training

✍ Jatinder N.D Gupta; Randall S Sexton 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 148 KB

A co-training algorithm for multi-view d

A co-training algorithm for multi-view data with applications in data fusion

✍ Mark Culp; George Michailidis 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 385 KB

## Abstract In several scientific applications, data are generated from two or more diverse sources (views) with the goal of predicting an outcome of interest. Often it is the case that the outcome is not associated with any single view. However, the synergy of all measurements from each view may y