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Investigation of Connection Between Deep Learning and Probabilistic Graphical Models

Paul Andrew Hager

出版	Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
URL	http://books.google.com.hk/books?id=C8_wvgEACAAJ&hl=&source=gbs_api

註釋

The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the link between these fields. The link focuses on probabilistic graphical models in the context of reinforcement learning. Viewing certain algorithms as reinforcement learning gives one an ability to map ML concepts to statistics problems. Training a multi-layer nonlinear perceptron algorithm is equivalent to structure learning problems in probabilistic graphical models (PGMs). The technique of boosting weak rules into an ensemble is weighted sampling. Finally regularizing neural networks using the dropout technique is conditioning on certain observations in PGMs.