השוואת שיטות

	פרספטרון רב-שכבתי (MLP)	XGBoost
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1986	2016
הוגה השיטה	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	Chen, T. & Guestrin, C.
סוג	Feedforward neural network (supervised learning)	Ensemble (gradient-boosted decision trees)
מקור מכונן	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
כינויים	MLP, feedforward neural network, fully connected neural network, artificial neural network	XGBoost, extreme gradient boosting, scalable tree boosting
קשורות	4	5
תקציר	The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.