Multi-layer Perceptron (MLP) Regression System
(Redirected from Multi-layer Perceptron Regression System)
A Multi-layer Perceptron (MLP) Regression System is a multilayer feedforward neural network training system that implements multi-layer perceptron regression algorithm to solve a Multi-layer Perceptron Regression Task.
- Example(s):
sklearn.neural_network.MLPRegressor(BaseMultilayerPerceptron, RegressorMixin)source code,- …
- Counter-Example(s):
- See: Feedforward Neural Network, Activation Function, Backpropagation, Perceptron.
References
2017a
- (sklearn,2017) ⇒ http://scikit-learn.org/stable/modules/neural_networks_supervised.html#regression Retrieved:2017-12-3.
- QUOTE: Class
MLPRegressorimplements a multi-layer perceptron (MLP) that trains using backpropagation with no activation function in the output layer, which can also be seen as using the identity function as activation function. Therefore, it uses the square error as the loss function, and the output is a set of continuous values.MLPRegressoralso supports multi-output regression, in which a sample can have more than one target.
- QUOTE: Class
2017b
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Multilayer_perceptron Retrieved:2017-12-3.
- A multilayer perceptron (MLP) is a class of feedforward artificial neural network. An MLP consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. [1] [2] Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.[3] Multilayer perceptrons are sometimes colloquially referred to as "vanilla" neural networks, especially when they have a single hidden layer. [4]
- ↑ Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
- ↑ Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams. “Learning Internal Representations by Error Propagation". David E. Rumelhart, James L. McClelland, and the PDP research group. (editors), Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundation. MIT Press, 1986.
- ↑ Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function Mathematics of Control, Signals, and Systems, 2(4), 303–314.
- ↑ Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, NY, 2009.
2014
- (Arouri, 2014) ⇒ Arouri, C., Nguifo, E. M., Aridhi, S., Roucelle, C., Bonnet-Loosli, G., & Tsopzé, N. (2014). "Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction". arXiv preprint arXiv:1412.5513.
- ABSTRACT: The choice of architecture of artificial neuron network (ANN) is still a challenging task that users face every time. It greatly affects the accuracy of the built network. In fact there is no optimal method that is applicable to various implementations at the same time. In this paper we propose a method to construct ANN based on clustering, that resolves the problems of random and ad hoc approaches for multilayer ANN architecture. Our method can be applied to regression problems. Experimental results obtained with different datasets, reveals the efficiency of our method