منابع مشابه
Decentralized Online Learning with Kernels
We consider multi-agent stochastic optimization problems over reproducing kernel Hilbert spaces (RKHS). In this setting, a network of interconnected agents aims to learn decision functions, i.e., nonlinear statistical models, that are optimal in terms of a global convex functional that aggregates data across the network, with only access to locally and sequentially observed samples. We propose ...
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Imbalanced learning, or learning from imbalanced data, is a challenging problem in both academy and industry. Nowadays, the streaming imbalanced data become popular and trigger the volume, velocity, and variety issues of learning from these data. To tackle these issues, online learning algorithms are proposed to learn a linear classifier via maximizing the AUC score. However, the developed line...
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We present two new algorithms for online learning in reproducing kernel Hilbert spaces. Our first algorithm, ILK (implicit online learning with kernels), employs a new, implicit update technique that can be applied to a wide variety of convex loss functions. We then introduce a bounded memory version, SILK (sparse ILK), that maintains a compact representation of the predictor without compromisi...
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This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels--online multiple ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2018
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2018.2830299