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Gaussian Processes for Machine Learning
Penulis: Carl Edward Rasmussen Christopher K. I. Williams •
Identifier: --carledward-1635
The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many ï¬Âelds, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientiï¬Âc and industrial ï¬Âelds. Recently, several research communities have converged on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. The MIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high-quality research and innovative applications