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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.1410
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Meta-learning has many aspects, but its final goal is to discover in an automatic way many interesting models for a given data. Our early attempts in this area involved heterogeneous learning systems combined with a complexity-guided search for optimal models, performed within the framework of (dis)similarity based ...
https://link.springer.com/chapter/10.1007/978-3-642-20980-2_10
Abstract. Meta-learning has many aspects, but its final goal is to discover in an automatic way many interesting models for a given data. Our early attempts in this area involved heterogeneous learning systems combined with a complexity-guided search for optimal models, performed within the framework of (dis)similarity based methods to discover “knowledge granules”.Cited by: 2
https://www.researchgate.net/publication/227263266_Optimal_Support_Features_for_Meta-Learning
Optimal Support Features for Meta-Learning. ... starting from new support features that are discovered by different types of data models created on similar tasks and successively building more ...
https://www.semanticscholar.org/paper/Optimal-Support-Features-for-Meta-Learning-Duch-Maszczyk/73b6c4c59bd4bd5d2a627cc9bed93da3f8b0acdb
Meta-learning has many aspects, but its final goal is to discover in an automatic way many interesting models for a given data. Our early attempts in this area involved heterogeneous learning systems combined with a complexity-guided search for optimal models, performed within the framework of (dis)similarity based methods to discover “knowledge granules”. This approach, inspired by ...
http://fizyka.umk.pl/publications/kmk/11-Features-Meta.pdf
1 Optimal Support Features for Meta-learning. 3 higher-order features, including high-level features derived from similarity to mem-orized prototypes or categories at some abstract level. In the space of such features knowledge is transferred between different tasks and used in solving problems that require sequential reasoning.Cited by: 2
http://fizyka.umk.pl/publications/kmk/11-Metalearning.pdf
meta-learning came with availability of large data mining packages such as Weka that contain hundreds of components (data transformations) that may be connected in millions of ways, making the problem of optimal model selection exceedingly difficult. Meta-learning
https://www.springer.com/gp/book/9783642209796
Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. ... Optimal Support Features for Meta-Learning. Pages 317-358. Duch, Włodzisław (et al.) Preview. Show next xx. Read this book on SpringerLink
http://usir.salford.ac.uk/34589/1/ShilBayehandVadera2014FeatureSelectionPaper.pdf
Feature Selection in Meta Learning Framework . Samar Shilbayeh . Department of Computer science and Engineering . ... weight to each feature then finds the optimal feature that exceeds a user threshold. Both of these filtering methods adopt ... Meta features: obtain the meta features for each data set . 2) Feature selection: apply the different ...
https://www.youtube.com/watch?v=2z0ofe2lpz4
Oct 22, 2018 · Meta learning describes the concept of 'learning to learn'. What if we could have AI learn how to optimize itself? An AI could learn the optimal hyper-parameters, architecture, and even dataset ...
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