השוואת שיטות

	רשת עצבית גרפית	סיווג תמונות באמצעות רשתות קונבולוציה (CNN)	מכונת וקטורים תומכים (סיווג)	XGBoost
תחום	למידה עמוקה	למידה עמוקה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning	Machine learning
שנת המקור	2017	2016	1995	2016
הוגה השיטה	Kipf, T.N. & Welling, M.	He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet)	Cortes, C. & Vapnik, V.	Chen, T. & Guestrin, C.
סוג	Deep learning on graph-structured data	Deep convolutional neural network (supervised)	Maximum-margin classifier (kernel method)	Ensemble (gradient-boosted decision trees)
מקור מכונן	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
כינויים	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet), convolutional neural network image classifier, deep image classification, ResNet / VGG / EfficientNet	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	XGBoost, extreme gradient boosting, scalable tree boosting
קשורות	4	5	5	5
תקציר	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	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.