Do you enjoy watching heavyweight boxing? Are you familiar with the many British names associated with this exciting sport? Would you like to find out more about the UK’s heavyweight champions, past and present, and all those who have battled to win this title? If you answered yes to any of these question, you are certain to enjoy this new quiz book all about British involvement in heavyweight boxing. Which British fighter fought for the world title in 1966 and was defeated in London and who was his opponent? In what year, and against which giant, did David Haye win the world title? To whom and in what round did Frank Bruno lose the WBC title in 1996? The answers to these and many more challenging questions can all be found in The Boxing Heavyweight Championship Quiz Book. This book will take you back to the early days of bare knuckle fighting right through to the current contenders for the boxing heavyweight championship title and is a must-have for all boxing fans of all ages.
Jersey Joe Walcott, Arturo Godoy and Tommy Farr. 89. A blacksmith. 90. He was
forty eight. His opponent was James Bonecrusher Smith. 91. Helston in Cornwall.
92. Don Cockell. 93. Canada. 94. He was born in Nigeria but moved to the UK as
a boy. His real name is Herbert Okechukwu Maduagwu. 95. Joe Bugner and
Richard Dunn. Bugner knocked out Dunn in the first round. 96. Savold won,
stopping Woodcock with a cut eye in the fourth round. 97. Jack Solomons. 98.
Doncaster.
The first cookbook on this hot health topic Trillions of bacteria naturally occur in the intestines, and most help protect the body from disease. These protective bacteria are called probiotics. Foods that nourish these "good" bacteria are called prebiotics. A number of factors can upset the balance between the levels of "good" and "bad" bacteria. There is evidence that consuming foods that are rich in "good" bacteria as well as foods that nourish these bacteria may help maintain a healthy balance of bacteria in the intestines and help improve health and fight certain diseases, like heart disease and cancer. This cookbook is organized by prebiotic and probiotic food recipes. Each of the 100 tasty recipes include instructions for properly cooking and storing food to preserve optimal levels of good bacteria.
The first cookbook on this hot health topic Trillions of bacteria naturally occur in the intestines, and most help protect the body from disease.
In this paper we describe how the co-author network, which is built from the
bibliographic records, can be incorporated into the process of personal name
language classification. The model is tested on the DBLP data set. The results
show that the extension of the language classification process with the co-author
network may help to refine the name language classification obtained from the
author names considered independently. It may also lead to the discovery of
dependencies ...
Domain Specific Modeling Languages (DSML) are more and more used to
handle high level concepts, and thus bring complex software development under
control. The increasingly recurring definition of new languages raises the
problem of the definition of support tools such as editor, simulator, compiler, etc.
In this paper we propose generative technologies that have been designed to
ease the development of model animation tools inside the TopCased platform.
These tools rely on ...
This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008. The 20 revised full papers presented together with the abstracts of 5 invited lectures were carefully reviewed and selected during two rounds of reviewing and improvement from 46 initial submissions. All current topics in inductive logic programming are covered, ranging from theoretical and methodological issues to advanced applications. The papers present original results in the first-order logic representation framework, explore novel logic induction frameworks, and address also new areas such as statistical relational learning, graph mining, or the semantic Web.
This can lead to subop- timal results given prediction tasks. On the other hand
better results in prediction problems have been achieved by discriminative
learning of MLNs weights given a certain structure. In this paper we propose an
algorithm for learning the structure of MLNs discriminatively by max- imimizing
the conditional likelihood of the query predicates instead of the joint likelihood of
all predicates. The algorithm chooses the structures by maximizing conditional
likelihood and ...
This book constitutes the refereed proceedings of the 10th Portuguese Conference on Artificial Intelligence, EPTA 2001, held in Porto, Portugal, in December 2001. The 21 revised long papers and 18 revised short papers were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections on extraction of knowledge from databases, AI techniques for financial time series analysis, multi-agent systems, AI logics and logic programming, constraint satisfaction, and AI planning.
This paper proposes a stochastic, and complete, backtrack search algorithm for
Propositional Satisfiability (SAT). In recent years, randomization has become
pervasive in SAT algorithms. Incomplete algorithms for SAT, for example the
ones based on local search, often re- sort to randomization. Complete algorithms
also resort to randomization. These include, state-of-the-art backtrack search SAT
algorithms that often randomize variable selection heuristics. Moreover, it is plain
that the ...
Shan-HweiNienhuys-Cheng(UniversityofRotterdam,Netherlands) WilliamCohen(WhizbangsLabs,USA) LucDeRaedt(UniversityofFreiburg,Germany) Sa?soD?zeroski(Jo?zefStefanInstitute,Ljubljana) PeterFlach(UniversityofBristol,UK) AlanFrisch(UniversityofYork,UK) KoichiFurukawa(UniversityofKeio,Japan) RoniKhardon(UniversityofEdinburgh,UK) J¨org-UweKietz(SwissLife,Switzerland) NadaLavra?c(Jo?zefStefanInstitute,Slovenia) JohnLloyd(AustralianNationalUniversity,Australia) StanMatwin(UniversityofOttawa,Canada) RaymondMooney(UniversityofTexas,USA) StephenMuggleton(UniversityofYork,UK) DavidPage(UniversityofWisconsin,USA) BernhardPfahringer(UniversityofWaikato,NewZealand) C´elineRouveirol(Universit´edeParis-Sud,France) ClaudeSammut(UniversityofNewSouthWales,Australia) ´ Mich`eleSebag(EcolePolytechnique,France) AshwinSrinivasan(UniversityofOxford,UK) PrasadTadepalli(OregonStateUniversity,USA) StefanWrobel(UniversityofMagdeburg,Germany) AkihiroYamamoto(UniversityofHokkaido,Japan) Additional Referees ´ ErickAlphonse(Universit´edeParis-Sud,France) LiviuBadea(NationalInstituteforResearchandDevelopmentinInformatics, Romania) DamjanDemsar(Jo?zefStefanInstitute,Slovenia) ElisabethGoncalves(Universit´edeParis-Sud,France) MarkoGrobelnik(Jo?zefStefanInstitute,Slovenia) ClaireKennedy(UniversityofBristol,UK) DanielKudenko(UniversityofYork,UK) JohanneMorin(UniversityofOttawa,Canada) TomonobuOzaki(KeioUniversity,Japan) EdwardRoss(UniversityofBristol,UK) LjupcoTodorovski(Jo?zefStefanInstitute,Slovenia) V´eroniqueVentos(Universit´edeParis-Sud,France) VIII ProgramCommitteeandReferees Sponsors of ILP2000 ILPNet2,TheEuropeanNetworkofExcellenceinInductiveLogicProgramming MLNet,TheEuropeanNetworkofExcellenceinMachineLearning CompulogNet,TheEuropeanNetworkofExcellenceinComputationalLogic Table of Contents IInvitedPaper ILP:JustDoIt. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 DavidPage II Contributed Papers ANewAlgorithmforLearningRangeRestrictedHornExpressions. . . . . . . 21 MartaArias,RoniKhardon ARe?nementOperatorforDescriptionLogics. . . . . . . . . . . . . . . . . . . . . . . . . 40 LiviuBadea,Shan-HweiNienhuys-Cheng ExecutingQueryPacksinILP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 HendrikBlockeel,LucDehaspe,BartDemoen,GerdaJanssens, JanRamon,HenkVandecasteele ALogicalDatabaseMiningQueryLanguage . . . . . . . . . . . . . . . . . . . . . . . . . . 78 LucDeRaedt Induction of Recursive Theories in the Normal ILP Setting: Issues and Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 FlorianaEsposito,DonatoMalerba,FrancescaA. Lisi ExtendingK-MeansClusteringtoFirst-OrderRepresentations. . . . . . . . . . . 112 MathiasKirsten,StefanWrobel TheoryCompletionUsingInverseEntailment . . . . . . . . . . . . . . . . . . . . . . . . . . 130 StephenH. Muggleton,ChristopherH. Bryant SolvingSelectionProblemsUsingPreferenceRelationBasedonBayesian Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 TomofumiNakano,NobuhiroInuzuka ConcurrentExecutionofOptimalHypothesisSearchforInverse Entailment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 HayatoOhwada,HiroyukiNishiyama,FumioMizoguchi UsingILPtoImprovePlanninginHierarchicalReinforcementLearning. . . 174 MarkReid,MalcolmRyan X TableofContents TowardsLearninginCARIN-ALN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 C´elineRouveirol,V´eroniqueVentos InverseEntailmentinNonmonotonicLogicPrograms. . . . . . . . . . . . . . . . . . . 209 ChiakiSakama ANoteonTwoSimpleTransformationsforImprovingtheE?ciencyofan ILPSystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 V´?torSantosCosta,AshwinSrinivasan,RuiCamacho SearchingtheSubsumptionLattic
10th International Conference, ILP 2000, London, UK, July 24-27, 2000
Proceedings James Cussens, Alan Frisch. A New Algorithm for Learning Range
Restricted Horn Expressions⋆ (Extended Abstract) Marta Arias and Roni
Khardon Division of Informatics, University of Edinburgh The King's Buildings,
Edinburgh EH9 3JZ, Scotland {marta ...
