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The book at hand gives an overview of the state of the art research
in Computational Sustainability as well as case studies of
different application scenarios. This covers topics such as
renewable energy supply, energy storage and e-mobility, efficiency
in data centers and networks, sustainable food and water supply,
sustainable health, industrial production and quality, etc. The
book describes computational methods and possible application
scenarios.
The book at hand gives an overview of the state of the art research
in Computational Sustainability as well as case studies of
different application scenarios. This covers topics such as
renewable energy supply, energy storage and e-mobility, efficiency
in data centers and networks, sustainable food and water supply,
sustainable health, industrial production and quality, etc. The
book describes computational methods and possible application
scenarios.
An intelligent agent interacting with the real world will encounter
individual people, courses, test results, drugs prescriptions,
chairs, boxes, etc., and needs to reason about properties of these
individuals and relations among them as well as cope with
uncertainty. Uncertainty has been studied in probability theory and
graphical models, and relations have been studied in logic, in
particular in the predicate calculus and its extensions. This book
examines the foundations of combining logic and probability into
what are called relational probabilistic models. It introduces
representations, inference, and learning techniques for
probability, logic, and their combinations. The book focuses on two
representations in detail: Markov logic networks, a relational
extension of undirected graphical models and weighted first-order
predicate calculus formula, and Problog, a probabilistic extension
of logic programs that can also be viewed as a Turing-complete
relational extension of Bayesian networks.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I (Paperback, 2013 ed.)
Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezny
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R1,700
Discovery Miles 17 000
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Ships in 10 - 15 working days
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This three-volume set LNAI 8188, 8189 and 8190 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2013, held in
Prague, Czech Republic, in September 2013. The 111 revised research
papers presented together with 5 invited talks were carefully
reviewed and selected from 447 submissions. The papers are
organized in topical sections on reinforcement learning; Markov
decision processes; active learning and optimization; learning from
sequences; time series and spatio-temporal data; data streams;
graphs and networks; social network analysis; natural language
processing and information extraction; ranking and recommender
systems; matrix and tensor analysis; structured output prediction,
multi-label and multi-task learning; transfer learning; bayesian
learning; graphical models; nearest-neighbor methods; ensembles;
statistical learning; semi-supervised learning; unsupervised
learning; subgroup discovery, outlier detection and anomaly
detection; privacy and security; evaluation; applications; and
medical applications.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II (Paperback, 2013 ed.)
Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezny
|
R1,697
Discovery Miles 16 970
|
Ships in 10 - 15 working days
|
This three-volume set LNAI 8188, 8189 and 8190 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2013, held in
Prague, Czech Republic, in September 2013. The 111 revised research
papers presented together with 5 invited talks were carefully
reviewed and selected from 447 submissions. The papers are
organized in topical sections on reinforcement learning; Markov
decision processes; active learning and optimization; learning from
sequences; time series and spatio-temporal data; data streams;
graphs and networks; social network analysis; natural language
processing and information extraction; ranking and recommender
systems; matrix and tensor analysis; structured output prediction,
multi-label and multi-task learning; transfer learning; bayesian
learning; graphical models; nearest-neighbor methods; ensembles;
statistical learning; semi-supervised learning; unsupervised
learning; subgroup discovery, outlier detection and anomaly
detection; privacy and security; evaluation; applications; and
medical applications.
One of the key open questions within arti?cial intelligence is how
to combine probability and logic with learning. This question is
getting an increased -
tentioninseveraldisciplinessuchasknowledgerepresentation,
reasoningabout uncertainty, data mining, and machine learning
simulateously, resulting in the
newlyemergingsub?eldknownasstatisticalrelationallearningandprobabil-
ticinductivelogicprogramming.Amajordriving forceisthe
explosivegrowth in the amount of heterogeneous data that is being
collected in the business and scienti?c world. Example domains
include bioinformatics, chemoinform- ics, transportation systems,
communication networks, social network analysis, linkanalysis,
robotics, amongothers.Thestructuresencounteredcanbeass-
pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure
predictionandnaturallanguageparsing)orascomplexascitationgraphs,
the WorldWideWeb, andrelationaldatabases. This book providesan
introduction to this ?eld with an emphasison those methods based on
logic programming principles. The book is also the main
resultofthesuccessfulEuropeanISTFETprojectno.FP6-508861onAppli-
tionofProbabilisticInductiveLogicProgramming(APRILII,2004-2007).This
projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Germany,
Luc De Raedt) and the partners were Imperial College London (UK,
Stephen MuggletonandMichaelSternberg),
theHelsinkiInstituteofInformationTe- nology(Finland,
HeikkiMannila), theUniversit adegliStudidiFlorence(Italy,
PaoloFrasconi), andtheInstitutNationaldeRechercheenInformatiqueet-
tomatiqueRocquencourt(France,
FrancoisFages).Itwasconcernedwiththeory,
implementationsandapplicationsofprobabilisticinductivelogicprogramming.
Thisstructureisalsore?ectedinthebook. The book starts with an
introductory chapter to "Probabilistic Inductive
LogicProgramming"byDeRaedtandKersting.Inasecondpart, itprovidesa
detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms
and systems. We are very pleased and proud that the scientists
behind the key probabilistic inductive logic programming systems
(also those developed outside the APRIL project) have kindly
contributed a chapter providing an
overviewoftheircontributions.Thisincludes:
relationalsequencelearningte- niques (Kersting et al.), using
kernels with logical representations (Frasconi andPasserini),
MarkovLogic(Domingosetal.), the PRISMsystem (Satoand Kameya),
CLP(BN)(SantosCostaetal.), BayesianLogicPrograms(Kersting
andDeRaedt), andtheIndependentChoiceLogic(Poole).Thethirdpartthen
provides a detailed account of some show-caseapplications of
probabilistic - ductive logic programming, more speci?cally: in
protein fold discovery (Chen et al.), haplotyping (Landwehr and
Mielik] ainen) and systems biology (Fages andSoliman). The ?nal
parttouchesupon sometheoreticalinvestigationsand VI Preface
includes chaptersonbehavioralcomparisonof
probabilisticlogicprogramming
representations(MuggletonandChen)andamodel-theoreticexpressivityan-
ysis(Jaege
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I (Paperback, 1st ed. 2021)
Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
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R3,122
Discovery Miles 31 220
|
Ships in 10 - 15 working days
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The 5-volume proceedings, LNAI 12457 until 12461 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2020, which was
held during September 14-18, 2020. The conference was planned to
take place in Ghent, Belgium, but had to change to an online format
due to the COVID-19 pandemic.The 232 full papers and 10 demo papers
presented in this volume were carefully reviewed and selected for
inclusion in the proceedings. The volumes are organized in topical
sections as follows: Part I: Pattern Mining; clustering; privacy
and fairness; (social) network analysis and computational social
science; dimensionality reduction and autoencoders; domain
adaptation; sketching, sampling, and binary projections; graphical
models and causality; (spatio-) temporal data and recurrent neural
networks; collaborative filtering and matrix completion. Part II:
deep learning optimization and theory; active learning; adversarial
learning; federated learning; Kernel methods and online learning;
partial label learning; reinforcement learning; transfer and
multi-task learning; Bayesian optimization and few-shot learning.
Part III: Combinatorial optimization; large-scale optimization and
differential privacy; boosting and ensemble methods; Bayesian
methods; architecture of neural networks; graph neural networks;
Gaussian processes; computer vision and image processing; natural
language processing; bioinformatics. Part IV: applied data science:
recommendation; applied data science: anomaly detection; applied
data science: Web mining; applied data science: transportation;
applied data science: activity recognition; applied data science:
hardware and manufacturing; applied data science: spatiotemporal
data. Part V: applied data science: social good; applied data
science: healthcare; applied data science: e-commerce and finance;
applied data science: computational social science; applied data
science: sports; demo track.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III (Paperback, 1st ed. 2021)
Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
|
R3,116
Discovery Miles 31 160
|
Ships in 10 - 15 working days
|
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2020, which was
held during September 14-18, 2020. The conference was planned to
take place in Ghent, Belgium, but had to change to an online format
due to the COVID-19 pandemic.The 232 full papers and 10 demo papers
presented in this volume were carefully reviewed and selected for
inclusion in the proceedings. The volumes are organized in topical
sections as follows: Part I: Pattern Mining; clustering; privacy
and fairness; (social) network analysis and computational social
science; dimensionality reduction and autoencoders; domain
adaptation; sketching, sampling, and binary projections; graphical
models and causality; (spatio-) temporal data and recurrent neural
networks; collaborative filtering and matrix completion. Part II:
deep learning optimization and theory; active learning; adversarial
learning; federated learning; Kernel methods and online learning;
partial label learning; reinforcement learning; transfer and
multi-task learning; Bayesian optimization and few-shot learning.
Part III: Combinatorial optimization; large-scale optimization and
differential privacy; boosting and ensemble methods; Bayesian
methods; architecture of neural networks; graph neural networks;
Gaussian processes; computer vision and image processing; natural
language processing; bioinformatics. Part IV: applied data science:
recommendation; applied data science: anomaly detection; applied
data science: Web mining; applied data science: transportation;
applied data science: activity recognition; applied data science:
hardware and manufacturing; applied data science: spatiotemporal
data. Part V: applied data science: social good; applied data
science: healthcare; applied data science: e-commerce and finance;
applied data science: computational social science; applied data
science: sports; demo track.
|
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part II (Paperback, 1st ed. 2021)
Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
|
R3,113
Discovery Miles 31 130
|
Ships in 10 - 15 working days
|
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2020, which was
held during September 14-18, 2020. The conference was planned to
take place in Ghent, Belgium, but had to change to an online format
due to the COVID-19 pandemic.The 232 full papers and 10 demo papers
presented in this volume were carefully reviewed and selected for
inclusion in the proceedings. The volumes are organized in topical
sections as follows: Part I: Pattern Mining; clustering; privacy
and fairness; (social) network analysis and computational social
science; dimensionality reduction and autoencoders; domain
adaptation; sketching, sampling, and binary projections; graphical
models and causality; (spatio-) temporal data and recurrent neural
networks; collaborative filtering and matrix completion. Part II:
deep learning optimization and theory; active learning; adversarial
learning; federated learning; Kernel methods and online learning;
partial label learning; reinforcement learning; transfer and
multi-task learning; Bayesian optimization and few-shot learning.
Part III: Combinatorial optimization; large-scale optimization and
differential privacy; boosting and ensemble methods; Bayesian
methods; architecture of neural networks; graph neural networks;
Gaussian processes; computer vision and image processing; natural
language processing; bioinformatics. Part IV: applied data science:
recommendation; applied data science: anomaly detection; applied
data science: Web mining; applied data science: transportation;
applied data science: activity recognition; applied data science:
hardware and manufacturing; applied data science: spatiotemporal
data. Part V: applied data science: social good; applied data
science: healthcare; applied data science: e-commerce and finance;
applied data science: computational social science; applied data
science: sports; demo track.
This SpringerBrief addresses the challenges of analyzing
multi-relational and noisy data by proposing several Statistical
Relational Learning (SRL) methods. These methods combine the
expressiveness of first-order logic and the ability of probability
theory to handle uncertainty. It provides an overview of the
methods and the key assumptions that allow for adaptation to
different models and real world applications. The models are highly
attractive due to their compactness and comprehensibility but
learning their structure is computationally intensive. To combat
this problem, the authors review the use of functional gradients
for boosting the structure and the parameters of statistical
relational models. The algorithms have been applied successfully in
several SRL settings and have been adapted to several real problems
from Information extraction in text to medical problems. Including
both context and well-tested applications, Boosting Statistical
Relational Learning from Benchmarks to Data-Driven Medicine is
designed for researchers and professionals in machine learning and
data mining. Computer engineers or students interested in
statistics, data management, or health informatics will also find
this brief a valuable resource.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III (Paperback, 2013 ed.)
Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezny
|
R1,696
Discovery Miles 16 960
|
Ships in 10 - 15 working days
|
This three-volume set LNAI 8188, 8189 and 8190 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2013, held in
Prague, Czech Republic, in September 2013. The 111 revised research
papers presented together with 5 invited talks were carefully
reviewed and selected from 447 submissions. The papers are
organized in topical sections on reinforcement learning; Markov
decision processes; active learning and optimization; learning from
sequences; time series and spatio-temporal data; data streams;
graphs and networks; social network analysis; natural language
processing and information extraction; ranking and recommender
systems; matrix and tensor analysis; structured output prediction,
multi-label and multi-task learning; transfer learning; bayesian
learning; graphical models; nearest-neighbor methods; ensembles;
statistical learning; semi-supervised learning; unsupervised
learning; subgroup discovery, outlier detection and anomaly
detection; privacy and security; evaluation; applications; and
medical applications.
Understanding the dynamics of collective human attention has been
called a key scientific challenge for the information age. Tackling
this challenge, Collective Attention on the Web explores the
dynamics of collective attention related to Internet phenomena such
as Internet memes, viral videos, or social media platforms and
Web-based businesses. To this end, it analyzes time series data
that directly or indirectly represent how the interest of large
populations of Web users in content or services develops over time.
Regardless of regional or cultural contexts, it generally observes
strong regularities in time series that reflect attention dynamics
and it discusses mathematical models that provide plausible
explanations as to what drives the apparently dominant dynamics of
rapid initial growth and prolonged decline.
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