Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings

449 Pages·2015·15.45 MB·English

by  Alexander Gammerman

Alexander Gammerman
Vladimir Vovk
Harris Papadopoulos (Eds.)
Statistical Learning
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Third International Symposium, SLDS 2015
Egham, UK, April 20–23, 2015
Proceedings
123
Lecture Notes in Artificial Intelligence 9047
Subseries of Lecture Notes in Computer Science
LNAISeries Editors
RandyGoebel
UniversityofAlberta,Edmonton,Canada
YuzuruTanaka
HokkaidoUniversity,Sapporo,Japan
WolfgangWahlster
DFKIandSaarlandUniversity,Saarbrücken,Germany
LNAIFoundingSeries Editor
JoergSiekmann
DFKIandSaarlandUniversity,Saarbrücken,Germany
Moreinformationaboutthisseriesathttp://www.springer.com/series/1244
·
Alexander Gammerman Vladimir Vovk
Harris Papadopoulos (Eds.)
Statistical Learning
and Data Sciences
Third International Symposium, SLDS 2015
Egham, UK, April 20–23, 2015
Proceedings
ABC
Editors
AlexanderGammerman HarrisPapadopoulos
UniversityofLondon FrederickUniversity
Egham,Surrey Nicosia
UK Cyprus
VladimirVovk
UniversityofLondon
Egham,Surrey
UK
ISSN0302-9743 ISSN1611-3349 (electronic)
LectureNotesinArtificialIntelligence
ISBN978-3-319-17090-9 ISBN978-3-319-17091-6 (eBook)
DOI10.1007/978-3-319-17091-6
LibraryofCongressControlNumber:2015935220
LNCSSublibrary:SL7–ArtificialIntelligence
SpringerChamHeidelbergNewYorkDordrechtLondon
(cid:2)c SpringerInternationalPublishingSwitzerland2015
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In memory of Alexey Chervonenkis
Preface
This volume contains the Proceedings of the Third Symposium on Statistical Learn-
ingandDataSciences,whichwasheldatRoyalHolloway,UniversityofLondon,UK,
duringApril20–23,2015.TheoriginalideaoftheSymposiumonStatisticalLearning
andDataSciencesisduetotwoFrenchacademics–ProfessorsMireilleGettlerSumma
andMyriamTouati–fromParisDauphineUniversity.Backin2009theythoughtthat
a “bridge” was required between various academic groups that were involved in re-
search on MachineLearning,Statistical Inference,PatternRecognition,Data Mining,
Data Analysis, and so on; a sort of multilayer bridge to connect those fields. This is
reflected in the symposiumlogo with the Passerelle Simone-de-Beauvoirbridge.The
ideawasimplementedandtheFirstSymposiumonStatisticalLearningandDataSci-
enceswasheldinParisin2009.Theeventwasindeedagreat“bridge”betweenvari-
ouscommunitieswithinterestingtalksbyJ.-P.Benzecri,V.Vapnik,A.Chervonenkis,
D.Hand,L.Bottou,andmanyothers.Papersbasedonthosetalkswerelaterpresented
inavolumeoftheModuladjournalandseparatelyinapost-symposiumbookentitled
StatisticalLearningandDataSciences,publishedbyChapman&Hall,CRCPress.The
secondsymposium,whichwasequallysuccessful,washeldinFlorence,Italy,in2012.
Overthelast6yearssincethefirstsymposium,theprogressinthetheoryandappli-
cationsoflearninganddatamininghasbeenveryimpressive.Inparticular,thearrivalof
technologiesforcollectinghugeamountsofdatahasraisedmanynewquestionsabout
how to store it and what type of analytics are able to handle it – what is now known
as Big Data. Indeed,the sheer scale of the data is very impressive– for example,the
LargeHadronCollidercomputershavetostore15petabytesayear(1petabyte=1015
bytes).Obviously,handlingthisrequirestheusageofdistributedclustersofcomputers,
streaming,parallelprocessing,andothertechnologies.Thisvolumeisconcernedwith
variousmoderntechniques,someofwhichcouldbeveryusefulforhandlingBigData.
The volume is divided into five parts. The first part is devoted to two invited pa-
persbyVladimirVapnik.Thefirstpaper,“LearningwithIntelligentTeacher:Similarity
ControlandKnowledgeTransfer,”isafurtherdevelopmentofhisresearchonlearning
with privileged information, with a special attention to the knowledge representation
problem. The second, “Statistical Inference Problems and their Rigorous Solutions,”
suggests a novel approach to pattern recognition and regression estimation. Both pa-
perspromisetobecomemilestonesinthedevelopingfieldofstatisticallearning.
The second part consists of 16 papers that were accepted for presentation at the
mainevent,whiletheotherthreepartsreflectnewresearchinimportantareasofstatis-
ticallearningtowhichthesymposiumdevotedspecialsessions.Specificallythespecial
sessionsincludedinthesymposium’sprogramwere:
– Special Session on Conformal Prediction and its Applications (CoPA 2015), or-
ganizedbyHarrisPapadopoulos(FrederickUniversity,Cyprus),AlexanderGam-
merman(RoyalHolloway,UniversityofLondon,UK),andVladimirVovk(Royal
Holloway,UniversityofLondon,UK).
VIII Preface
– SpecialSessiononNewFrontiersinDataAnalysisforNuclearFusion,organized
byJesusVega(AsociacionEURATOM/CIEMATparaFusion,Spain).
– SpecialSessiononGeometricDataAnalysis,organizedbyFionnMurtagh(Gold-
smithCollegeLondon,UK).
Overall, 36 papers were accepted for presentation at the symposium after being re-
viewed by at least two independent academic referees. The authors of these papers
comefrom17differentcountries,namely:Brazil,Canada,Chile,China,Cyprus,Fin-
land,France,Germany,Greece,Hungary,India,Italy,Russia,Spain,Sweden,UK,and
USA.
A special session at the symposium was devoted to the life and work of Alexey
Chervonenkis,whotragicallydiedinSeptember2014.He wasoneofthefoundersof
modernMachineLearning,abelovedcolleagueandfriend.Allhislifehewasconnected
withtheInstituteofControlProblemsinMoscow,overthelast15yearsheworkedat
Royal Holloway, University of London, while over the last 7 years he also worked
for the Yandex Internet company in Moscow. This special session included talks in
memoryofAlexeybyVladimirVapnik–hislongstandingcolleagueandfriend–and
byAlexey’sformerstudentsandcolleagues.
WeareverygratefultotheProgramandOrganizingCommittees,thesuccessofthe
symposiumwouldhavebeenimpossiblewithouttheirhardwork.Weareindebtedtothe
sponsors:theRoyalStatisticalSociety,theBritishComputerSociety,theBritishClassi-
ficationSociety,RoyalHolloway,UniversityofLondon,andParisDauphineUniversity.
Our specialthanksto Yandexfortheirhelp andsupportin organizingthesymposium
and the special session in memory of Alexey Chervonenkis. This volume of the pro-
ceedingsofthesymposiumisalsodedicatedtohismemory.Restinpeace,dearfriend.
February2015 AlexanderGammerman
VladimirVovk
HarrisPapadopoulos
Organization
General Chairs
AlexanderGammerman,UK
VladimirVovk,UK
Organizing Committee
ZhiyuanLuo,UK MyriamTouati,France
MireilleSumma,France JanetHales,UK
YuriKalnishkan,UK
ProgramCommitteeChairs
HarrisPapadopoulos,Cyprus
XiaohuiLiu,UK
FionnMurtagh,UK
ProgramCommitteeMembers
VineethBalasubramanian,India ShenshyangHo,Singapore
GiacomoBoracchi,Italy CarloLauro,Italy
PaulaBrito,Portugal GuangLi,China
LéonBottou,USA DavidLindsay,UK
LarsCarlsson,Sweden HenrikLinusson,Sweden
JaneChang,UK Hans-J.Lenz,Germany
FrankCoolen,UK IliaNouretdinov,UK
GertdeCooman,Belgium MatildeSantos,Spain
JesusManueldelaCruz,Spain VictorSolovyev,SaudiArabia
Jose-CarlosGonzalez-Cristobal,Spain JesusVega,Spain
AnnaFukshansky,Germany RosannaVerde,Italy
BarbaraHammer,Germany
Contents
Invited Papers
Learning with Intelligent Teacher: Similarity Control and Knowledge
Transfer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Vladimir Vapnik and Rauf Izmailov
Statistical Inference Problems and Their Rigorous Solutions . . . . . . . . . . . . 33
Vladimir Vapnik and Rauf Izmailov
Statistical Learning and its Applications
Feature Mapping Through Maximization of the Atomic Interclass
Distances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Savvas Karatsiolis and Christos N. Schizas
Adaptive Design of Experiments for Sobol Indices Estimation Based
on Quadratic Metamodel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Evgeny Burnaev and Ivan Panin
GoldenEye++: A Closer Look into the Black Box . . . . . . . . . . . . . . . . . . . 96
Andreas Henelius, Kai Puolamäki, Isak Karlsson, Jing Zhao,
Lars Asker, Henrik Boström, and Panagiotis Papapetrou
Gaussian Process Regression for Structured Data Sets. . . . . . . . . . . . . . . . . 106
Mikhail Belyaev, Evgeny Burnaev, and Yermek Kapushev
Adaptive Design of Experiments Based on Gaussian Processes . . . . . . . . . . 116
Evgeny Burnaev and Maxim Panov
Forests of Randomized Shapelet Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
Isak Karlsson, Panagotis Papapetrou, and Henrik Boström
Aggregation of Adaptive Forecasting Algorithms Under Asymmetric
Loss Function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Alexey Romanenko
Visualization and Analysis of Multiple Time Series by Beanplot PCA . . . . . 147
Carlo Drago, Carlo Natale Lauro, and Germana Scepi
Recursive SVM Based on TEDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Dmitry Kangin and Plamen Angelov