Microstructure Cluster Analysis with Transfer Learning and Unsupervised Learning
نویسندگان
چکیده
منابع مشابه
Unsupervised and Transfer Learning
We organized a data mining challenge in “unsupervised and transfer learning” (the UTL challenge) followed by a workshop of the same name at the ICML 2011 conference in Bellevue, Washington1. This introduction presents the highlights of the outstanding contributions that were made, which are regrouped in this issue of JMLR W&CP. Novel methodologies emerged to capitalize on large volumes of unlab...
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Traditional supervised learning algorithms typically assume that the training data and test data come from a common underlying distribution. Therefore, they are challenged by the mismatch between training and test distributions encountered in transfer learning situations. The problem is further exacerbated when the test data actually comes from a different domain and contains no labeled example...
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Clustering analysis is widely used in many fields. Traditionally clustering is regarded as unsupervised learning for its lack of a class label or a quantitative response variable, which in contrast is present in supervised learning such as classification and regression. Here we formulate clustering as penalized regression with grouping pursuit. In addition to the novel use of a non-convex group...
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Learning good representations from a large set of unlabeled data is a particularly challenging task. Recent work (see Bengio (2009) for a review) shows that training deep architectures is a good way to extract such representations, by extracting and disentangling gradually higher-level factors of variation characterizing the input distribution. In this paper, we describe different kinds of laye...
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Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. The objective is to make these higherlevel representations more abstract, with their individual features more invariant to most of the variations that are typical...
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ژورنال
عنوان ژورنال: Integrating Materials and Manufacturing Innovation
سال: 2018
ISSN: 2193-9764,2193-9772
DOI: 10.1007/s40192-018-0116-9