We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves online model selection oracle inequalities under minimal structural assumption...

The Perceptron algorithm, despite its simplicity, often performs well on online classification tasks. The Perceptron becomes especially effective when it is used in conjunction with kernels. However, a common difficulty encountered when implementing kernel-based online algorithms is the amount of memory required to store the online hypothesis, which may grow unboundedly. In this paper we presen...

Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric. Recently, a number of studies have proposed online bipartite ranking algorithms to learn from massive streams of class-imbalanced data. These methods suggest both linear and kernel-based bipartite ranking algorithms based on first and second-order online learning...

We consider the problem of tracking minimum a time-varying convex optimization over dynamic graph. Motivated by target and parameter estimation problems in intermittently connected robotic sensor networks, goal is to design distributed algorithm capable handling nondifferentiable regularization penalties. The proposed proximal online gradient descent built run fully decentralized manner utilize...

introduction: motor skills play an important role during life span, and older adults need to learn or relearn these skills. the purpose of this study was to investigate how aging affects induction of improved movement performance by motor training. methods: serial reaction time test (srtt) was used to assess movement performance during 8 blocks of motor training. participants were tested in two...

In this work, we focus on Online Sparse Kernel Learning that aims to online learn a kernel classifier with a bounded number of support vectors. Although many online learning algorithms have been proposed to learn a sparse kernel classifier, most of them fail to bound the number of support vectors used by the final solution which is the average of the intermediate kernel classifiers generated by...

the present study compares the performance of two tefl reading classes: one taking place in a regular classroom and the other held in a computer lab, with the learners practicing reading online. the results of an independent samples t-test showed that the difference between the learners’ scores on their reading comprehension post-tests and pretests did not differ statistically significantly fro...

The paper presents two useful extensions of the incremental SVM in the context of online learning. An online support vector data description algorithm enables application of the online paradigm to unsupervised learning. Furthermore, online learning can be used in the large-scale classification problems to limit the memory requirements for storage of the kernel matrix. The proposed algorithms ar...

We resolve an open question from (Christiano, 2014b) posed in COLT’14 regarding the optimal dependency of the regret achievable for online local learning on the size of the label set. In this framework, the algorithm is shown a pair of items at each step, chosen from a set of n items. The learner then predicts a label for each item, from a label set of size L and receives a real valued payoff. ...