Titlebook: Learning to Learn; Sebastian Thrun,Lorien Pratt Book 1998 Springer Science+Business Media New York 1998 algorithms.artificial neural netwo

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Richard Maclin,Jude W. Shavlikuses particularly on science learning at the interface betwe.Higher education internationally is in a state of transition and transformation, leading to an increase in the level of participation, and a consequent increase in number of non traditional and underprepared students. The appearance of the

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oach is able to utilize complex, diverse and high-dimensional data sets, which often occur in manufacturing applications, and to integrate the important process intra- and interrelations. The approach has been evaluated using three scenarios from different manufacturing domains (aviation, chemical a

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A Survey of Connectionist Network Reuse Through Transferk on transfer. A number of distinctions between kinds of transfer are identified, and future directions for research are explored. The study of transfer has a long history in cognitive science. Discoveries about transfer in human cognition can inform applied efforts. Advances in applications can als

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Multitask Learning of . tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. This paper reviews prior work on MTL, presents new evidence that MTL in backprop nets discovers task relatedness wit

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Making a Low-Dimensional Representation Suitable for Diverse Tasksneralization via a better low-dimensional representation of the problem space. The quality of the representation is assessed by embedding it in a 2D space using multidimensional scaling, allowing a direct visualization of the results. The performance of the approach is demonstrated on a highly nonli