Σύγκριση μεθόδων

	Πολυεπίπεδο Εμπειρογνώμονας (MLP)	Τυχαίο Δάσος	Αναδρομικό Νευρωνικό Δίκτυο
Πεδίο	Βαθιά Μάθηση	Μηχανική Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	1986	2001	1986–1990
Δημιουργός	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	Breiman, L.	Rumelhart, D. E.; Elman, J. L.
Τύπος	Supervised feedforward neural network	Ensemble (bagging of decision trees)	Sequential neural network
Θεμελιώδης πηγή	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Εναλλακτικές ονομασίες	MLP, feedforward neural network, fully connected neural network, vanilla neural network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	RNN, Elman network, Jordan network, simple recurrent network
Συναφείς	4	4	3
Σύνοψη	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.