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How the neural network is useful in classification?
A neural network is a sequence of algorithms that endeavors to identify basic relationships in a set of data through a process that mimics the approach the human brain works. In this method, neural networks define systems of neurons, either organic or artificial.
Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations after implementing a process of so-called learning from existing information. Neural Networks is one of the Data Mining techniques.
A neural network is an array of algorithms that endeavors to identify fundamental relationships in a set of data through a process that mimics the techniques the human brain operates. In this sense, neural networks define systems of neurons, such as organic or artificial.
Neural networks are relevant in virtually every situation in which a relationship among the predictor variables (independents, inputs) and predicted variables (dependents, outputs) endure, even when that relationship is difficult and not easy to articulate in the general terms of "correlations" or "differences among groups."
A neural network is a network of simulated neurons that is used to identify instances of patterns. Neural networks understand by searching through an area of network weights.
A neural network is a set of linked input/output units where each link has a weightrelated to it. During the learning procedure, the networks learn by adjusting weights to be capable to forecast the accurate class label of the input samples. NN learning is also referred to as connection learning due to the connections between units.
Neural networks needed long training items, they have been reviewed for their poor interpretability because it is complex to interpret the symbolic meaning following the learning weights. These features originally create neural networks less fascinating for data mining.
The neural network includes their high tolerance to noisy data and their ability to classify patterns on which they have not been trained. There are various algorithms have been developed for the extraction of rules from trained neural networks. These elements contribute towards the convenience of neural networks for classification in data mining.
In engineering, neural networks deliver two important functions as pattern classifiers and as non-linear adaptive filters. An Artificial Neural Network is a flexible, most often non-linear system that understands to implement a function (an input/output map) from data. Adaptive defines that the system parameters are transformed during operation, generally known as the training phase.
After the training phase, the Artificial Neural Network parameters are constant and the system is set up to solve the issue at hand (the testing phase). The Artificial Neural Network is developed with a systematic step-by-step phase to enhance a performance test or to follow some implicit internal constraint, which is generally defined as the learning rule.
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