Post-retrieval clustering is the task of clustering Web search results. Within this context, we propose a new methodology that adapts the classical K-means algorithm to a third-order similarity measure initially developed for NLP tasks. Results obtained with the definition of a new stopping criterion over the ODP-239 and the MORESQUE gold standard datasets evidence that our proposal outperforms...

A/B testing refers to the task of determining the best option among two alternatives that yield random outcomes. We provide distribution-dependent lower bounds for the performance of A/B testing that improve over the results currently available both in the fixed-confidence (or δ-PAC) and fixed-budget settings. When the distribution of the outcomes are Gaussian, we prove that the complexity of t...

This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a test...

This work employs the Brownian motion model in which observations are taken sequentially. The objective is to detect a two-sided change in the constant drift by means of a stopping rule. As a performance measure, an extended Lorden criterion is used. The goal is to minimize the worst-case detection delay subject to a constraint in the frequency of false alarms. In a companion paper, attention i...

An iterative univariate minimizer (line search) is often used to generate a steplength in each step of a descent method for minimizing a multivariate function. The line search performance strongly depends on the choice of the stopping rule enforced. This termination criterion and other algorithmic details also affect the overall efficiency of the multivariate minimization procedure. Here we pro...

Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. In this paper we propose a novel ear...

In this paper, a traditional clustering algorithm based on speaker identification is presented. Several audio data sets were tested to conclude how accurate the clustering algorithm is depending on the characteristics of the analyzed database. We show that, issues such as the size of the database, the number speakers, or how the audios are balanced over the speakers in the database significantl...

We present a general analysis for the criteria to stop and store light coherently. We show that a light pulse can be stopped in any physical system, provided that (i) the system bandwidth can be compressed to zero; (ii) the system has sufficient degrees of freedom to accommodate the pulse, and the bandwidth compression occurs while the pulse is in the system; and (iii) the bandwidth compression...