- linear threshold function
- линейная пороговая функция
Англо-русский словарь по компьютерной безопасности. Академик.ру. 2011.
Англо-русский словарь по компьютерной безопасности. Академик.ру. 2011.
Linear discriminant analysis — (LDA) and the related Fisher s linear discriminant are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The… … Wikipedia
Linear least squares — is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to measurements obtained from experiments. The goals of linear least squares are to extract predictions from the… … Wikipedia
Linear least squares/Proposed — Linear least squares is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to observations obtained from experiments. Mathematically, it can be stated as the problem of… … Wikipedia
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Artificial neuron — An artificial neuron is a mathematical function conceived as a crude model, or abstraction of biological neurons. Artificial neurons are the constitutive units in an artificial neural network. Depending on the specific model used, it can receive… … Wikipedia
Feedforward neural network — A feedforward neural network is an artificial neural network where connections between the units do not form a directed cycle. This is different from recurrent neural networks.The feedforward neural network was the first and arguably simplest… … Wikipedia
optimization — /op teuh meuh zay sheuhn/ 1. the fact of optimizing; making the best of anything. 2. the condition of being optimized. 3. Math. a mathematical technique for finding a maximum or minimum value of a function of several variables subject to a set of … Universalium
Support vector machine — Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Viewing input data as two sets of vectors in an n dimensional space, an SVM will construct a separating hyperplane in that… … Wikipedia