This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose s...
Numerous data mining problems involve an investigation of associations between features in heterogeneous datasets, where different prediction models can be more suitable for differ...
Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Pan...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
We present an efficient search strategy for satisfiability checking on circuits represented at the register-transfer-level (RTL). We use the RTL circuit structure by extending con...
Ganapathy Parthasarathy, Madhu K. Iyer, Kwang-Ting...
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
Lihong Li, Michael L. Littman, Christopher R. Mans...