✦ LIBER ✦

A review of supervised machine learning algorithms and their applications to ecological data

✍ Scribed by C. Crisci; B. Ghattas; G. Perera

Book ID: 113578723
Publisher: Elsevier Science
Year: 2012
Tongue: English
Weight: 560 KB
Volume: 240
Category: Article
ISSN: 0304-3800
DOI: 10.1016/j.ecolmodel.2012.03.001

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A review on particle swarm optimization

A review on particle swarm optimization algorithms and their applications to data clustering

✍ Sandeep Rana; Sanjay Jasola; Rajesh Kumar 📂 Article 📅 2010 🏛 Springer Netherlands 🌐 English ⚖ 247 KB

Ensemble Machine Learning Volume 423 ||

Ensemble Machine Learning Volume 423 || Boosting Algorithms: A Review of Methods, Theory, and Applications

✍ Zhang, Cha; Ma, Yunqian 📂 Article 📅 2012 🏛 Springer US 🌐 English ⚖ 901 KB

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness a

Algorithmic transformations for neural c

Algorithmic transformations for neural computing and performance of supervised learning on a dataflow machine

✍ Kim, S.T.; Suwunboriruksa, K.; Herath, S.; Jayasumana, A.; Herath, J. 📂 Article 📅 1992 🏛 IEEE 🌐 English ⚖ 1016 KB

[IEEE 2010 International Conference on M

[IEEE 2010 International Conference on Machine Learning and Applications (ICMLA) - Washington, DC, USA (2010.12.12-2010.12.14)] 2010 Ninth International Conference on Machine Learning and Applications - Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - A Case Study with Medicinal Chemistry Datasets

✍ Karunaratne, Thashmee; Bostrom, Henrik; Norinder, Ulf 📂 Article 📅 2010 🏛 IEEE 🌐 English ⚖ 303 KB

Application of machine learning techniqu

Application of machine learning techniques to the analysis of soil ecological data bases: relationships between habitat features and Collembolan community characteristics

✍ Christian Kampichler; Sašo Džeroski; Ralf Wieland 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 385 KB

On tree types of competitive learning al

On tree types of competitive learning algorithms with their comparisons and applications to MRI segmentation

✍ Miin-Shen Yang; Kuo-Lung Wu; Karen Chia-Ren Lin; Hsiu-Chih Liu; Jiing-Feng Lirng 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 378 KB

This paper considers competitive learning networks using three types of hard, soft, and fuzzy learning schemes. The hard competitive learning algorithm is with the winner-take-all. The soft competition learning algorithm is with a stochastic relaxation technique using the Gibbs distribution as a dyn