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...

Road detection is a crucial problem in the application of autonomous vehicle and on-road mobile robot. Most of the recent methods only achieve reliable results in some particular well-arranged environments. In this paper, we describe a road detection algorithm for front-view monocular camera using road probabilistic distribution model (RPDM) and online learning method. The primary contribution ...

It is easy to design on-line learning algorithms for learning k out of n variable monotone disjunctions by simply keeping one weight per disjunction. Such algorithms use roughly O(n) weights which can be prohibitively expensive. Surprisingly, algorithms like Winnow require only n weights (one per variable) and the mistake bound of these algorithms is not too much worse than the mistake bound of...

In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning ...

In the last three lectures we have been discussing the online learning algorithms where we receive the instance x and then its label y for t = 1, ..., T . Specifically in the last lecture we talked about online learning from experts and online prediction. We saw many algorithms like Halving algorithm, Weighted Majority (WM) algorithm and lastly Weighted Majority Continuous (WMC) algorithm. We a...

We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of our algorithm and on the number of labels it queries when faced with an adaptive adversarial strategy of generating the instances. Our bounds both generalize and strictly improve over previous bounds in similar settings....

We compare three approaches to learning numerical parameters of Bayesian networks from continuous data streams: (1) the EM algorithm applied to all data, (2) the EM algorithm applied to data increments, and (3) the online EM algorithm. Our results show that learning from all data at each step, whenever feasible, leads to the highest parameter accuracy and model classification accuracy. When fac...

Introduction. Continuous learning and online decisionmaking in complex dynamic environments under conditions of uncertainty and limited computational recourses represent one of the most challenging problems for developing robust intelligent systems. The existing task of unsupervised clustering in statistical learning requires the maximizing (or minimizing) of a certain similarity-based objectiv...

An on line learning algorithm for reinforcement learning with continually running recur rent networks in non stationary reactive environments is described Various kinds of rein forcement are considered as special types of input to an agent living in the environment The agent s only goal is to maximize the amount of reinforcement received over time Supervised learning techniques for recurrent ne...

We present a series of algorithms with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones. We also report the results of experiments with these algorithms on various structured predic...