The volume of data that humans create has increased explosively as information science and technology have evolved. Therefore, the demand for learning machines that can extract input-output mappings and knowledge rules from massive data sets has become more urgent, and machine learning is now a core technology in the advanced information society. It has been applied to fields such as pattern re...

Introduction: Postgraduate medical education involves the use ofonline-learning tools. However, there is a paucity of data on theuse of online-learning among doctors who are in their 1st and 2ndyears of professional work after graduating from medical school(also known as Foundation doctors). Our aim was to explore theuse of online-learning among Foundation doctors.Methods: A cross-sectional stu...

The task of assigning label sequences to a set of observed sequences is common in computational linguistics. Several models for sequence labeling have been proposed over the last few years. Here, we focus on discriminative models for sequence labeling. Many batch and online (updating model parameters after visiting each example) learning algorithms have been proposed in the literature. On large...

Efficient learning from massive amounts of information is a hot topic in computer vision. Available training sets contain many examples with several visual descriptors, a setting in which current batch approaches are typically slow and does not scale well. In this work we introduce a theoretically motivated and efficient online learning algorithm for the Multi Kernel Learning (MKL) problem. For...

We study and compare different neural network learning strategies: batch-mode learning, online learning, cyclic learning, and almost-cyclic learning. Incremental learning strategies require less storage capacity than batch-mode learning. However, due to the arbitrariness in the presentation order of the training patterns, incremental learning is a stochastic process; whereas batch-mode learning...

We describe, analyze, and experiment with a framework for empirical loss minimization with regularization. Our algorithmic framework alternates between two phases. On each iteration we first perform an unconstrained gradient descent step. We then cast and solve an instantaneous optimization problem that trades off minimization of a regularization term while keeping close proximity to the result...

We introduce new online and batch algorithms that are robust to data with missing features, a situation that arises in many practical applications. In the online setup, we allow for the comparison hypothesis to change as a function of the subset of features that is observed on any given round, extending the standard setting where the comparison hypothesis is fixed throughout. In the batch setup...