evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

The present paper investigates the problem of prediction in the context of dynamically changing environment, where data arrive over time. A Dynamic online Ensemble Learning Algorithm (DELA) is introduced. The adaptivity concerns three levels: structural adaptivity, combination adaptivity and model adaptivity. In particular, the structure of the ensemble is sought to evolve in order to be able t...

This paper develops an efficient online algorithm based on K-SVD for learning multiple consecutive tasks. We first derive a batch multi-task learning method that builds upon the K-SVD algorithm, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current lifelong learning algorithms while m...

We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stem from recent advances in online learning algorithms. The algorithms are simple to implement and are also time and memory efficient. We provide a unified analysis of the family of algorithms in the mistake bound model. We then discuss experiments with the proposed family of top...

In this paper we propose a cascading framework for optimizing online learning in machine translation for a computer assisted translation scenario. With the use of online learning, several hyperparameters associated with the learning algorithm are introduced. The number of iterations of online learning can affect the translation quality as well. We discuss these issues and propose a few approach...

A major obstacle in using Latent Dirichlet Allocation (LDA) is the amount of time it takes for inference, especially for a dataset that starts out large and expands quickly, such as a corpus of blog posts or online news articles. Recent developments in distributed inference algorithms for LDA, as well as minibatchbased online learning algorithms have offered partial solutions for problem. In th...

Background: One of the main competencies required for enabling Nursing students to provide effective clinical care is spiritual health. The growth and development of nursing students’ spiritual health rely on strengthening their cognitive and metacognitive components. What is more associated with spirituality and spiritual health is students’ metacognition. This study aimed to investigate the e...

We study the problem of online multi-class learning with partial feedback: in each trial of online learning, instead of providing the true class label for a given instance, the oracle will only reveal to the learner if the predicted class label is correct. We present a general framework for online multi-class learning with partial feedback that adapts the potential-based gradient descent approa...

In this paper, a novel noise-constrained least-squares (NCLS) method for online autoregressive (AR) parameter estimation is developed under blind Gaussian noise environments, and a discrete-time learning algorithm with a fixed step length is proposed. It is shown that the proposed learning algorithm converges globally to an AR optimal estimate. Compared with conventional second-order and high-o...

We describe online algorithms for learning a rotation from pairs of unit vectors in R. We show that the expected regret of our online algorithm compared to the best fixed rotation chosen offline over T iterations is O( √ nT ). We also give a lower bound that proves that this expected regret bound is optimal within a constant factor. This resolves an open problem posed in COLT 2008. Our online a...