Top Document: comp.ai.neural-nets FAQ, Part 1 of 7: Introduction Previous Document: How are layers counted? Next Document: What are the population, sample, training set, See reader questions & answers on this topic! - Help others by sharing your knowledge A vector of values presented at one time to all the input units of a neural network is called a "case", "example", "pattern, "sample", etc. The term "case" will be used in this FAQ because it is widely recognized, unambiguous, and requires less typing than the other terms. A case may include not only input values, but also target values and possibly other information. A vector of values presented at different times to a single input unit is often called an "input variable" or "feature". To a statistician, it is a "predictor", "regressor", "covariate", "independent variable", "explanatory variable", etc. A vector of target values associated with a given output unit of the network during training will be called a "target variable" in this FAQ. To a statistician, it is usually a "response" or "dependent variable". A "data set" is a matrix containing one or (usually) more cases. In this FAQ, it will be assumed that cases are rows of the matrix, while variables are columns. Note that the often-used term "input vector" is ambiguous; it can mean either an input case or an input variable. User Contributions:Top Document: comp.ai.neural-nets FAQ, Part 1 of 7: Introduction Previous Document: How are layers counted? Next Document: What are the population, sample, training set, Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Part7 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
Last Update March 27 2014 @ 02:11 PM
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