We propose the contextual combinatorial cascading bandits, a combinatorial online learning game, where at each time step a learning agent is given a set of contextual information, then selects a list of items, and observes stochastic outcomes of a prefix in the selected items by some stopping criterion. In online recommendation, the stopping criterion might be the first item a user selects; in ...

In training artificial neural networks, Backpropagation has been frequently used and known to provide powerful tools for classification. Due to its capability to model linear and non-linear systems, it is widely applied to various areas, offering solutions and help to human experts. However, BP still has shortcomings and a lot of studies had already been done to overcome it. But one of the impo...

This work compares the performance of all existing 2-CUSUM stopping rules used in the problem of sequential detection of a change in the drift of a Brownian motion in the case of two-sided alternatives. As a performance measure an extended Lorden criterion is used. According to this criterion the optimal stopping rule is an equalizer rule. This paper compares the performance of the modified dri...

1 Department of Communications, Faculty of Electronics and Telecommunications, Bd. V. Pârvan Nr. 2, 300223 Timişoara, e-mail: [email protected], [email protected] 2 Electronic Department,ENST Bretagne Technopôle de Brest Iroise CS 83 818 29238 BREST Cedex 3, France, e-mail: [email protected] Abstract – This paper presents a simple and efficient criterion for stopping the itera...

Agglomerative hierarchical clustering (AHC) is an unsupervised classification strategy of merging the closest pair of clusters recursively, and has been widely used in speaker diarization systems to classify speech segments by speaker identity. The most critical part in AHC is how to automatically stop the recursive process at the point when clustering error rate reaches its lowest possible val...

We consider an optimal stopping problem with a discrete time stochastic process where a criterion is a threshold probability. We first obtain the fundamental characterization of an optimal value and an optimal stopping time as the result of the classical optimal stopping problem, but the optimal value and the optimal stopping time depend upon a threshold value. We also give the properties of th...

Active learning is a proven method for reducing the cost of creating the training sets that are necessary for statistical NLP. However, there has been little work on stopping criteria for active learning. An operational stopping criterion is necessary to be able to use active learning in NLP applications. We investigate three different stopping criteria for active learning of named entity recog...

This letter proposes an efficient and simple stopping criterion for turbo decoding, which is derived by observing the behavior of log-likelihood ratio (LLR) values. Based on the behavior, the proposed criterion counts the number of absolute LLR values less than a threshold and the number of hard decision 1’s in order to complete the iterative decoding procedure. Simulation results show that the...

In this paper, we study an inexact inverse iteration with inner-outer iterations for solving the generalized eigenvalue problem Ax = Bx; and analyze how the accuracy in the inner iterations aaects the convergence of the outer iterations. By considering a special stopping criterion depending on a threshold parameter, we show that the outer iteration converges linearly with the threshold paramete...