We have recently been studying the case where have a training set T generated from an underlying distribution and our goal is to find some good hypothesis, with respect to the true underlying distribution, using the training set T . We now examine how to use online learning algorithms (which work on individual, arbitrary sequences) in a stochastic setting. Let us consider the training set T as ...

Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinforcement learning show that transfer may significantly improve the learning performance, most of the literature on transfer is focused on batch learning tasks. In this paper we study the problem of sequential transfer...

We introduce a coe cient update procedure into existing batch and online dictionary learning algorithms. We rst propose an algorithm which is a coe cient updated version of the Method of Optimal Directions (MOD) dictionary learning algorithm (DLA). The MOD algorithm with coe cient updates presents a computationally expensive dictionary learning iteration with high convergence rate. Secondly, we...

In a previous paper [8] we have proposed a method to improve the classification between two classes in a new transformed space using the Chernoff similarity measure. The key idea is to estimate a transformation matrix such that the overlap between the pdf associated to the competing classes is minimum thus leading to a minimization of the classification error. Starting from a surrogate cost fun...

this paper illustrates the design and implementation of fain (filtering agent for internet) based on the gaia methodology and the auml notation. the proposed system generates and maintains a user profile by learning from examples that are judged and classified by the user during the training stage. a learning algorithm which is based on the baldwin effect that combines learning with evolution i...

Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news. A key task in the automated identification of such news is the detection of novel documents from a voluminous stream of text documents in a scalable manner. Motivated by this challenge, we introduce the problem of online `1-dictionary learning where unlike traditional dictionary learning, wh...

We develop a novel visual behaviour modelling approach that performs incremental and adaptive behaviour model learning for online abnormality detection. Three key features make our approach advantageous over previous ones: (1) unsupervised learning, (2) online and incremental model construction, and (3) model adaptation to changes in visual context. In particular, we formulate an incremental EM...

Wearable technologies play a central role in human-centered Internet-of-Things applications. Wearables leverage machine learning algorithms to detect events of interest such as physical activities and medical complications. A major obstacle in large-scale utilization of current wearables is that their computational algorithms need to be re-built from scratch upon any changes in the configuratio...