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Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject's body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare(More)
Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this(More)
The problem of assessing the performance of a classifier, in the finite-sample setting, has been addressed by Vapnik in his seminal work by using data-independent measures of complexity. Recently, several authors have addressed the same problem by proposing data-dependent measures, which tighten previous results by taking in account the actual data(More)
In-sample approaches to model selection and error estimation of support vector machines (SVMs) are not as widespread as out-of-sample methods, where part of the data is removed from the training set for validation and testing purposes, mainly because their practical application is not straightforward and the latter provide, in many cases, satisfactory(More)
— In–sample model selection for Support Vector Machines is a promising approach that allows using the training set both for learning the classifier and tuning its hyperparame-ters. This is a welcome improvement respect to out–of–sample methods, like cross–validation, which require to remove some samples from the training set and use them only for model(More)
— A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing effective SVM model selection. This fact is supported by experience, because well–known hold–out methods like cross–validation, leave–one–out, and the bootstrap usually achieve better results than the ones derived from MLT. We show in this paper that, in a(More)
The Maximal Discrepancy and the Rademacher Complexity are powerful statistical tools that can be exploited to obtain reliable, albeit not tight, upper bounds of the generalization error of a classifier. We study the different behavior of the two methods when applied to linear classifiers and suggest a practical procedure to tighten the bounds. The resulting(More)
The purpose of this paper is to obtain a fully empirical stability-based bound on the generalization ability of a learning procedure, thus, circumventing some limitations of the structural risk minimization framework. We show that assuming a desirable property of a learning algorithm is sufficient to make data-dependency explicit for stability, which,(More)