Artificial neural network and Multiclass classification

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Difference between Artificial neural network and Multiclass classification

Artificial neural network vs. Multiclass classification

Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Not to be confused with multi-label classification. In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes.

Extreme learning machine

Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need not be tuned.

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K-nearest neighbors algorithm

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.

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Machine learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

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Multilayer perceptron

A multilayer perceptron (MLP) is a class of feedforward artificial neural network.

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Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update our best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.

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Perceptron

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).

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Softmax function

In mathematics, the softmax function, or normalized exponential function, is a generalization of the logistic function that "squashes" a -dimensional vector \mathbf of arbitrary real values to a -dimensional vector \sigma(\mathbf) of real values, where each entry is in the range (0, 1, and all the entries add up to 1. The function is given by In probability theory, the output of the softmax function can be used to represent a categorical distribution – that is, a probability distribution over different possible outcomes. In fact, it is the gradient-log-normalizer of the categorical probability distribution. The softmax function is also the gradient of the LogSumExp function. The softmax function is used in various multiclass classification methods, such as multinomial logistic regression (also known as softmax regression), multiclass linear discriminant analysis, naive Bayes classifiers, and artificial neural networks. Specifically, in multinomial logistic regression and linear discriminant analysis, the input to the function is the result of distinct linear functions, and the predicted probability for the 'th class given a sample vector and a weighting vector is: This can be seen as the composition of linear functions \mathbf \mapsto \mathbf^\mathsf\mathbf_1, \ldots, \mathbf \mapsto \mathbf^\mathsf\mathbf_K and the softmax function (where \mathbf^\mathsf\mathbf denotes the inner product of \mathbf and \mathbf). The operation is equivalent to applying a linear operator defined by \mathbf to vectors \mathbf, thus transforming the original, probably highly-dimensional, input to vectors in a -dimensional space \mathbb^K.

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Statistical classification

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

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Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

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