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A Statistical Learning Theory Framework for Supervised Pattern Discovery
[article]
2014
arXiv
pre-print
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This paper formalizes a latent variable inference problem we call supervised pattern discovery, the goal of which is to find sets of observations that belong to a single "pattern." ...
The bounds for the second version of the problem are stated in terms of a new complexity measure, the quasi-Rademacher complexity. ...
Acknowledgements Thanks to Dylan Kotliar and Yakir Reshef for helpful discussions regarding personalized medicine and cancer genomics and to Peter Krafft for helpful comments. ...
arXiv:1307.0802v2
fatcat:wiivcrz6ybbdnek7k7g22jb63m
A Statistical Learning Theory Framework for Supervised Pattern Discovery
[chapter]
2014
Proceedings of the 2014 SIAM International Conference on Data Mining
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This paper formalizes a latent variable inference problem we call supervised pattern discovery, the goal of which is to find sets of observations that belong to a single "pattern." ...
These processes are allowed to take an arbitrary form, so observations within a pattern are not in general independent of each other. ...
Acknowledgements Thanks to Dylan Kotliar and Yakir Reshef for helpful discussions regarding personalized medicine and cancer genomics and to Peter Krafft for helpful comments. ...
doi:10.1137/1.9781611973440.58
dblp:conf/sdm/HugginsR14
fatcat:ok5bn3jrgjhfrdy6cn6qnxeoci
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
[article]
2020
arXiv
pre-print
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This performance is attributed to our novel self-supervised learning framework, encouraging deep models to learn compelling semantic representation from abundant anatomical patterns resulting from consistent ...
But how exactly such strong yet free semantics embedded in medical images can be harnessed for self-supervised learning remains largely unexplored. ...
We thank Zuwei Guo for implementing Rubik's cube, and Jiaxuan Pang for evaluating I3D. The content of this paper is covered by patents pending. ...
arXiv:2007.06959v1
fatcat:aikw2jksvrbnvjigjucjgk7nya
Explanation-Oriented Association Mining Using a Combination of Unsupervised and Supervised Learning Algorithms
[chapter]
2003
Lecture Notes in Computer Science
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The mining task can be viewed as unsupervised learning that searches for interesting patterns. ...
The proposed framework is therefore a simple combination of unsupervised learning and supervised learning. The basic ideas are illustrated using association mining. ...
We present a new explanation-oriented framework for data mining by combining unsupervised and supervised learning. For clarity, we use association mining to demonstrate the basic ideas. ...
doi:10.1007/3-540-44886-1_45
fatcat:afgh64acazarbdqefncd6dl634
From discrete element simulation data to process insights
2021
EPJ Web of Conferences
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This study explores the potential of statistical learning to identify potential regions of interest for large scale discrete element simulations. ...
Industrial-scale discrete element simulations typically generate Gigabytes of data per time step, which implies that even opening a single file may require 5 - 15 minutes on conventional magnetic storage ...
We developed an initial KDD framework for existing and new granular processes, where we do not opt for conventional supervised learning approaches. ...
doi:10.1051/epjconf/202124915001
fatcat:uhjw4lxarnc3lbg7kb76bnc5dm
Guest editorial: Special issue on data mining for medicine and healthcare
2015
Data mining and knowledge discovery
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Additionally, we would like to thank Geoff Webb, Editor-in-Chief and editorial staff of the Data Mining and Knowledge Discovery journal for their support and guidance in the production of this special ...
Acknowledgments Guest editors of this special issue would like to thank all authors of 49 submitted papers, as well as all reviewers for their hard work and detailed reviews that led to eight accepted ...
Authors demonstrate a successful application of the proposed transfer learning framework to estimate the risk for diabetes on the state level. ...
doi:10.1007/s10618-015-0414-1
fatcat:ewzmk4wogza6dlwmrfo7tagdp4
Review of Machine Learning Approaches to Semantic Web Service Discovery
2010
Journal of Advances in Information Technology
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A thorough analysis of existing frameworks for semantic discovery of Web Services is provided in the paper. ...
This paper provides an exhaustive review of machine learning approaches used for Web Services discovery and frameworks developed based on these approaches. ...
MACHINE LEARNING BASED FRAMEWORKS In machine learning there are two major settings in which a function can be described: supervised learning and unsupervised learning. ...
doi:10.4304/jait.1.3.146-151
fatcat:lhdvgen5bfh2jhb37nrx6emptm
Data Mining in Biomedicine: Current Applications and Further Directions for Research
2009
Journal of Software Engineering and Applications
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Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. ...
This paper introduces some basic data mining techniques, unsupervised learning and supervising learning and reviews the application of data mining in biomedicine. ...
The authors would like to express their sincere thanks to the Research Committee of The Hong Kong Polytechnic University for financial support of the research work presented in this paper. ...
doi:10.4236/jsea.2009.23022
fatcat:24qp2zpq2nbdhl22fzgtg7apwy
Special issue on selected papers from IEEE DMF 2008
2010
Knowledge and Information Systems
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They cover the research areas in peer-to-peer networks, optimization-based data mining, graph clustering, and semi-supervised learning. ...
many years of effort in the R&D of new data mining methodologies, new processes in knowledge discovery, new theory on data mining foundations and new insights into the use of data mining techniques in ...
The last paper "Semi-Supervised Learning by Disagreement" by Zhi-Hua Zhou and Ming Li proposes solutions for semi-supervised learning. ...
doi:10.1007/s10115-010-0339-3
fatcat:amgowlo7vbhalfck32galynsrq
Advancing mathematics by guiding human intuition with AI
2021
Nature
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Since the 1960s, mathematicians have used computers to assist in the discovery of patterns and formulation of conjectures1, most famously in the Birch and Swinnerton-Dyer conjecture2, a Millennium Prize ...
We propose a process of using machine learning to discover potential patterns and relations between mathematical objects, understanding them with attribution techniques and using these observations to ...
Vinyals, A. Gaunt, A. Fawzi and D. Saxton for advice and comments on early drafts; J. Vonk for contemporary supporting work; X. Glorot and M. Overlan for insight and assistance; and A. Pierce, N. ...
doi:10.1038/s41586-021-04086-x
pmid:34853458
pmcid:PMC8636249
fatcat:chmva4bzwve7tejeog76rxdvwm
A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery
2022
Materials
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This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery ...
Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments' challenges in a time and cost-efficient manner. ...
The supervised learning models find a function that has a capacity for novel material discovery using known materials and their properties. ...
doi:10.3390/ma15041428
pmid:35207968
pmcid:PMC8875409
fatcat:43rsdj5gnnf6rhqfr6qlnm2syq
The Inscrutability of Reference
[chapter]
2001
Inquiries into Truth and Interpretation
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We cover approaches that make use of constraints for partitional and hierarchical algorithms to both enforce the constraints or to learn a distance function from the constraints. REFERENCES ABE, N. ...
Query learning strategies using boosting and bagging. In ...
YAN, R., ZHANG, J., YANG, J., AND HAUPTMANN, A. G. 2004. A discriminative learning framework with pairwise constraints for video object classification. ...
doi:10.1093/0199246297.003.0016
fatcat:m527offlcngibl3t55hzie2raq
The Inscrutability of Reference
1979
Southwestern Journal of Philosophy
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We cover approaches that make use of constraints for partitional and hierarchical algorithms to both enforce the constraints or to learn a distance function from the constraints. REFERENCES ABE, N. ...
Query learning strategies using boosting and bagging. In ...
YAN, R., ZHANG, J., YANG, J., AND HAUPTMANN, A. G. 2004. A discriminative learning framework with pairwise constraints for video object classification. ...
doi:10.5840/swjphil197910229
fatcat:uafnl7hhynaf5czc2xoj66lgja
Brain Network Architecture: Implications for Human Learning
[article]
2016
arXiv
pre-print
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framework for education and therapy. ...
In this review, we discuss the utility of network neuroscience as a tool to build a quantitative framework in which to study human learning, which seeks to explain the full chain of events in the brain ...
ACKNOWLEDGEMENTS We thank Ari Kahn for comments on an earlier version of this manuscript. The work was supported by the NSF BCS-1430087. We also acknowledge support from the John D. and Catherine T. ...
arXiv:1609.01790v1
fatcat:6iasvkq4m5hbfkhsp2lm2dnikm
Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges
2021
Complex & Intelligent Systems
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Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. ...
This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. ...
Semi-supervised learning Semi-supervised learning is a combination of supervised and unsupervised learning techniques. ...
doi:10.1007/s40747-021-00342-9
fatcat:ak3y4ao2sbffjd5b3rbttidvjy
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