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Formulation of energy efficient protocols is of utmost importance for wireless sensor networks because of energy constraints of sensor nodes. When a number of nodes is deployed in a field located away from the base station, the nodes undergo unequal energy dissipation while transmitting information to the base station primarily due to two reasons: i) the difference in the distances of nodes from the...
Factorization Machine (FM) provides a generic framework that combines the prediction quality of factorization models with the flexibility of feature engineering that discriminative models like SVM offer. The Bayesian Factorization Machine [11], with its impressive predictive performance and the convenience of automatic tuning of parameters, has been one of the most successful and efficient approaches...
Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, we investigate how to make an existing algorithm, named C3E (from Combining Classifier and Cluster Ensembles), more user-friendly by automatically tunning its main parameters with the use of metaheuristics. In particular, the...
The paper presents a real time car detection and tracking system that can be used in a mobile device equipped with a camera (e.g. handset, tablet etc.). The underlying detector is implemented using a variant of the AdaBoost [5], and the tracker is built on Lucas-Kanade optical flow algorithm. The uniqueness of the work lies in the simplicity and the portability of the framework that can assist drivers...
This paper introduces a privacy-aware Bayesian approach that combines ensembles of classifiers and clusterers to perform semi-supervised and transductive learning. We consider scenarios where instances and their classification/clustering results are distributed across different data sites and have sharing restrictions. As a special case, the privacy aware computation of the model when instances of...
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