In this paper, we study the problem of fine-grained image categorization. The goal of our method is to explore fine image statistics and identify the discriminative image patche...
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, S...
We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multipl...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
To observe, analyze and control large scale distributed systems and the applications hosted on them, there is an increasing need to continuously monitor performance attributes of ...
Shicong Meng, Srinivas R. Kashyap, Chitra Venkatra...