Linganisha mbinu

	Mafunzo ya Metriki ya Mtandaoni	Jifunze Mtandaoni	Mtandao wa Kifamilia wa Neural (Siamese Neural Network)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	2004–2009	1958–2000s	1993
Mwanzilishi	Shalev-Shwartz, S.; Singer, Y.; and others	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
Aina	Online / incremental learning of distance metrics	Learning paradigm (sequential model update)	Deep metric-learning architecture
Chanzo asilia	Shalev-Shwartz, S., Singer, Y., & Ng, A. Y. (2004). Online and batch learning of pseudo-metrics. Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pp. 94. ACM.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.
Majina mbadala	OML, incremental metric learning, streaming metric learning, online distance metric learning	incremental learning, sequential learning, streaming learning, online machine learning	twin network, Siamese neural network, contrastive metric network, Siyam ağı
Zinazohusiana	3	6	1
Muhtasari	Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale, or continually changing environments where retraining from scratch is impractical.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.