J. Yu, S. Vishwanathan, S. Günter, and N. N. Schraudolph. A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. Journal of Machine Learning Research, 11:1145–1200, 2010.
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Semantic Scholar extracted view of "Learning To Learn: Introduction" by Sebastian Thrun. Sebastian Thrun · View PDF. Share This L Y Pratt, B Jennings. Learning to learn. S Thrun, L Pratt. Springer Science & Business Media, 2012. 829, 2012. Comparing biases for minimal network construction with back- 18 Nov 2015 PDF | This paper introduces the application of gradient descent methods to Download full-text PDF Meta-learning is a framework to learn a learning algorithm under a certain distribution (Thrun and Pratt 1998; Hochreiter, PDF | The field of meta-learning has as one of its primary goals the understanding of the interaction between the Download full-text PDF weexpectthelearningmechanismitselftore-learn, takingintoaccountprevious. METALEARNING 3. experience (Thrun, 1998; Pratt and Jennings, 1998; Caruana, 1997; Vilalta and. Drissi cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of long history [Thrun and Pratt, 1998]. More recently, Lake et al.
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Originally published in 2006, Kaehler's book Learning OpenCV (O'Reilly) serves as an introduction to the library and its use.
