Online Boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being trackedt. It is a promising technique for o...
This paper considers online stochastic reservation problems, where requests come online and must be dynamically allocated to limited resources in order to maximize profit. Multi-k...
Pascal Van Hentenryck, Russell Bent, Yannis Vergad...
Abstract. In this paper, we consider two new online optimization problems (each with several variants), present similar online algorithms for both, and show that one reduces to the...
All high-performance production JVMs employ an adaptive strategy for program execution. Methods are first executed unoptimized and then an online profiling mechanism is used to ...
Dries Buytaert, Andy Georges, Michael Hind, Matthe...
A self-adaptive Hidden Markov Model (SA-HMM) based framework is proposed for behavior recognition in this paper. In this model, if an unknown sequence cannot be classified into an...