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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)
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)
In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector(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 model selection for Support Vector Machines is a promising approach that allows using the training set both for learning the classifier and tuning its hyperparameters. 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)
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 Structural Risk Minimization framework has been recently proposed as a practical method for model selection in Support Vector Machines (SVMs). The main idea is to effectively measure the complexity of the hypothesis space, as defined by the set of possible classifiers, and to use this quantity as a penalty term for guiding the model selection process.(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)