Since numerous biological and environmental factors are involved in a complex manner in ecological processes, data collected from field surveys or laboratory experiments in ecology are analytically complex. Appropriate understanding of ecological data, however, is critical in objectively characterizing ecological systems at issue (e.g., pollution, pest infestation) and in providing useful information for ecosystem monitoring and management.

Artificial neural networks, based on supervised and unsupervised learning, is an alternative tool for ecological data processing. While supervised learning is carried out for data estimation (e.g., prediction, revealing the environment-community causality relationships) based on a priori knowledge (i.e., templates), unsuper-vised learning is useful in extracting information from the data (e.g., ordination, classification) without previous knowledge. Especially, self-organizing maps (SOMs) based on the Kohonen network are extensively used in the extraction of information from ecological data. In this article, the principles and application of the SOM are outlined along with examples to demonstrate patterning and visualization resulting from the network.

10 Ways To Fight Off Cancer

10 Ways To Fight Off Cancer

Learning About 10 Ways Fight Off Cancer Can Have Amazing Benefits For Your Life The Best Tips On How To Keep This Killer At Bay Discovering that you or a loved one has cancer can be utterly terrifying. All the same, once you comprehend the causes of cancer and learn how to reverse those causes, you or your loved one may have more than a fighting chance of beating out cancer.

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