It is desirable to find unusual data objects by Ramaswamy et al's distance-based outlier definition because only a metric distance function between two objects is required. It...
Local search (LS) algorithms are among the most powerful techniques for solving computationally hard problems in combinatorial optimization. These algorithms could be viewed as &q...
Recently, there has been a surge of interest in algorithms that allocate advertisement space in an online revenue-competitive manner. Most such algorithms, however, assume a pay-a...
Ashish Goel, Mohammad Mahdian, Hamid Nazerzadeh, A...
We study local, distributed algorithms for the capacitated minimum dominating set (CapMDS) problem, which arises in various distributed network applications. Given a network graph...
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal...
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, P...
We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which prec...
Subramani Mani, Constantin F. Aliferis, Alexander ...
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...
Pulse singularities with variable amplitudes appear in the corresponding positions of ignition coil primary current waveform when insulation fault occurs during the ignition proce...