The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it "a priori" because the processed data se...
Motivated by issues of saving energy in data centers we define a collection of new problems referred to as "machine activation" problems. The central framework we introd...
Abstract. This paper presents a new classification algorithm for realtime inference of emotions from the non-verbal features of speech. It identifies simultaneously occurring emoti...
The automatic tuning of the parameters of algorithms and automatic selection of algorithms has received a lot of attention recently. One possible approach is the use of machine lea...
The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opp...
The acceleration of rhythm of everyday life requires efficiency and flexibility in daily routines. The real expectations and needs of people concerning intelligent home devices sh...
This paper considers the scheduling problems arising in two- and three-machine manufacturing cells configured in a flowshop which repeatedly produces one type of product and where ...
The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed ...
Robert Stevens, Carole A. Goble, Ian Horrocks, Sea...
We consider a two node multiclass queueing network given by two machines each with two classes. There are two streams of jobs: One stream originates in machine 1, which feeds it f...