Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for real-world prediction tasks. The most popular random forest variants (such as ...

The online learning of deep neural networks is an interesting problem of machine learning because, for example, major IT companies want to manage the information of the massive data uploaded on the web daily, and this technology can contribute to the next generation of lifelong learning. We aim to train deep models from new data that consists of new classes, distributions, and tasks at minimal ...

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...

We investigate online active learning techniques for classification tasks in data stream mining applications. Unlike traditional learning approaches (either batch or online learning) that often require to request the class label of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, which aims to maximize cla...

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 i...

Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with realworld data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-o...

We present a novel method for learning Support Vector Machines (SVMs) in the online setting. Our method is generally applicable in that it handles the online learning of the binary, multiclass, and structural SVMs in a unified view. The SVM learning problem consists of optimizing a convex objective function that is composed of two parts: the hinge loss and quadratic (L2) regularization. To date...

We present the optimization package in the FACTORIE library for machine learning, graphical models, and natural language processing in Scala. The library is designed with an eye towards the way practitioners naturally break down the structure of a learning problem, giving first-class status to, for example, training examples, update rules, and regularization. It supports flexible mixing and mat...