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Generalizing over Several Learning Settings [chapter]

Anna Kasprzik
2010 Lecture Notes in Computer Science  
We present a meta-algorithm that generalizes over as many settings involving one or more of those information sources as possible and covers the whole range of combinations allowing inference with polynomial  ...  We recapitulate regular one-shot learning from membership and equivalence queries, positive and negative finite data.  ...  GENMODEL offers itself to be extended in several directions.  ... 
doi:10.1007/978-3-642-15488-1_28 fatcat:hfjjdztaljd6rgid4pbyc5psdq

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

C. R. Milare, G. E. A. P. A. Batista, A. C. P. L. F. Carvalho
2010 Logic Journal of the IGPL  
These balanced data sets are given to classical Machine Learning systems that output rule sets.  ...  We create several balanced data sets with all minority class cases and a random sample of majority class cases.  ...  Several papers have analyzed the problem of learning from imbalance data sets (for instance [24, 20, 19, 12, 18, 17, 4] ).  ... 
doi:10.1093/jigpal/jzq027 fatcat:fo3k4zs6dreffgxsmxipbmz7sq

Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

Mekides Assefa Abebe
2021 London Imaging Meeting  
Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.  ...  The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details.  ...  [26] and 114 SDR over-exposed image sets generated by Steffens et. al  ... 
doi:10.2352/issn.2694-118x.2021.lim-43 fatcat:mvwb7omjm5fe5ldtqnbzgiej7q

Learning a Severity Score for Sepsis: A Novel Approach based on Clinical Comparisons

Kirill Dyagilev, Suchi Saria
2015 AMIA Annual Symposium Proceedings  
Additionally, the learned score is sensitive to changes in severity leading up to septic shock and post treatment administration.  ...  Recently, we proposed the Disease Severity Score Learning (DSSL) framework that automatically derives a severity score from data based on clinical comparisons - pairs of disease states ordered by their  ...  Conclusion In this paper we evaluated the feasibility of automatically learning a score (L-DSS-Sepsis) that tracks the severity of sepsis over time.  ... 
pmid:26958288 pmcid:PMC4765650 fatcat:qnn2lb6eyvaylasikze3tqah6e

An open-set speaker identification system using genetic learning classifier system

WonKyung Park, Jae C. Oh, Misty K. Blowers, Matt B. Wolf
2006 Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06  
This paper presents the design and implementation of an adaptive open-set speaker identification system with genetic learning classifier systems.  ...  We also identify several challenges for learning classifier systems in the speaker identification problem and introduce new methods to improve the learning and classification abilities of the systems.  ...  We also experimented with several different encoding methods including hyperspheres, hyperellipsoids, and general hyperellipsoids [1] .  ... 
doi:10.1145/1143997.1144259 dblp:conf/gecco/ParkOBW06 fatcat:dlcgx7c6vjakbgwahxoem6nazq

Learning with Feature Description Logics [chapter]

Chad M. Cumby, Dan Roth
2003 Lecture Notes in Computer Science  
This paradigm provides a natural solution to the problem of learning and representing relational data; it extends and unifies several lines of works in KRR and Machine Learning in ways that provide hope  ...  We introduce a Feature Description Logic (FDL) -a relational (frame based) language that supports efficient inference, along with a generation function that uses inference with descriptions in the FDL  ...  , and outputs a set of active features over & .  ... 
doi:10.1007/3-540-36468-4_3 fatcat:h6frg4h3hrhcnm44tg2i7mmiiy

Page 547 of None Vol. 26, Issue 6 [page]

1940 None  
Rats, for example, learn mazes, puzzles, and discrimination habits better when practice is spaced over several intervals.  ...  Recent reviews of the literature (2) (18) reveal that most investi- gators have found practice distributed over several sittings more economical of learning time than practice concentrated into a single  ... 

On multiple-instance learning of halfspaces

D.I. Diochnos, R.H. Sloan, Gy. Turán
2012 Information Processing Letters  
An Ω(d log r) lower bound is given for the VC-dimension of bags of size r for d-dimensional halfspaces and it is shown that the same lower bound holds for halfspaces over any large point set in general  ...  In multiple-instance learning the learner receives bags, i.e., sets of instances. A bag is labeled positive if it contains a positive example of the target.  ...  Acknowledgement: We would like to thank Robert Langlois, Hans Ulrich Simon, and Balázs Szörényi for several interesting discussions.  ... 
doi:10.1016/j.ipl.2012.08.017 fatcat:b2qodm6j2vazblmzmd53arwk64

Learning Constraint Satisfaction Problems: An ILP Perspective [chapter]

Luc De Raedt, Anton Dries, Tias Guns, Christian Bessiere
2016 Lecture Notes in Computer Science  
In this note, we point out several similarities and differences between the two classes of techniques and use these to propose several interesting research challenges.  ...  We investigate the problem of learning constraint satisfaction problems from an inductive logic programming perspective.  ...  Therefore, several researchers are learning CSPs from queries [6, 4] and from small sets of examples ( [11, 3] ).  ... 
doi:10.1007/978-3-319-50137-6_5 fatcat:ghzndcw4qfeu7pk5fnnt2nay54

A Framework to Hybridize PBIL and a Hyper-heuristic for Dynamic Environments [chapter]

Gönül Uludağ, Berna Kiraz, A. Şima Etaner-Uyar, Ender Özcan
2012 Lecture Notes in Computer Science  
The experimental results over well known benchmark instances show that the approach is generalized enough to provide a good average performance over different types of dynamic environments.  ...  This study investigates the performance of approaches based on a framework that hybridizes selection hyper-heuristics and population based incremental learning (PBIL), mixing offline and online learning  ...  To generate different environments using the XOR generator, a set of M XOR masks are randomly generated.  ... 
doi:10.1007/978-3-642-32964-7_36 fatcat:uorxvsw7bbayvp2hpdfhkwv5tq

Learning disease severity for capsule endoscopy images

R. Kumar, P. Rajan, S. Bejakovic, S. Seshamani, G. Mullin, T. Dassopoulos, G. Hager
2009 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.  ...  Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images.  ...  Previous work on machine learning has generally made use of some combination of color and texture features.  ... 
doi:10.1109/isbi.2009.5193306 dblp:conf/isbi/KumarRBSMDH09 fatcat:smtwodpjs5h6fb5vvvxr2ewuby

Ensemble Classification for Constraint Solver Configuration [chapter]

Lars Kotthoff, Ian Miguel, Peter Nightingale
2010 Lecture Notes in Computer Science  
This paper investigates the differences in performance of several techniques on different data sets.  ...  Little research has been done into which machine learning algorithms are suitable and the impact of picking the "right" over the "wrong" technique.  ...  Acknowledgements We thank Chris Jefferson for the description of one of the problem attributes used in the analysis, Jesse Hoey for useful discussions about machine learning, and anonymous reviewers for  ... 
doi:10.1007/978-3-642-15396-9_27 fatcat:4jyoqxia2zdixi54yynan7x2ty

BAYESIAN DATA INTEGRATION: A FUNCTIONAL PERSPECTIVE

Curtis Huttenhower, Olga G. Troyanskaya
2006 Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006  
These learning techniques are not specific to Bayesian networks, and thus our conclusions should generalize to other methods for data integration.  ...  This study considers the effect of network structure and compares expert estimated conditional probabilities with those learned using a generative method (expectation maximization) and a discriminative  ...  Using two network structures (naive and full), three parameter estimation methods (expert estimation, generative EM learning, and discriminative ELR learning), and several data sets, we demonstrate that  ... 
doi:10.1142/18609475730044 fatcat:7rsz6xxsf5hfjn2ymkpkh64jca

BAYESIAN DATA INTEGRATION: A FUNCTIONAL PERSPECTIVE

Curtis Huttenhower, Olga G. Troyanskaya
2006 Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006  
These learning techniques are not specific to Bayesian networks, and thus our conclusions should generalize to other methods for data integration.  ...  This study considers the effect of network structure and compares expert estimated conditional probabilities with those learned using a generative method (expectation maximization) and a discriminative  ...  Using two network structures (naive and full), three parameter estimation methods (expert estimation, generative EM learning, and discriminative ELR learning), and several data sets, we demonstrate that  ... 
doi:10.1142/1860947573_0044 fatcat:qr67ouqwrrb2fnzr5viemaohke

BAYESIAN DATA INTEGRATION: A FUNCTIONAL PERSPECTIVE

Curtis Huttenhower, Olga G. Troyanskaya
2006 Computational Systems Bioinformatics  
These learning techniques are not specific to Bayesian networks, and thus our conclusions should generalize to other methods for data integration.  ...  This study considers the effect of network structure and compares expert estimated conditional probabilities with those learned using a generative method (expectation maximization) and a discriminative  ...  Using two network structures (naive and full), three parameter estimation methods (expert estimation, generative EM learning, and discriminative ELR learning), and several data sets, we demonstrate that  ... 
doi:10.1142/9781860947575_0041 fatcat:geaxbam3b5cmxcngic3nzctjyq
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