Machine Learning and Knowledge Discovery in Databases (häftad)
Fler böcker inom
Häftad (Paperback / softback)
Antal sidor
1st ed. 2019
Springer Nature Switzerland AG
Berlingerio, Michele (ed.), Bonchi, Francesco (ed.), Gärtner, Thomas (ed.), Hurley, Neil (ed.), Ifrim, Georgiana (ed.)
Bibliographie 227 schwarz-weiße Abbildungen
192 Illustrations, color; 271 Illustrations, black and white; XXX, 866 p. 463 illus., 192 illus. in
234 x 156 x 45 mm
1235 g
Antal komponenter
1 Paperback / softback
Machine Learning and Knowledge Discovery in Databases (häftad)

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II

Häftad Engelska, 2019-01-23
Skickas inom 10-15 vardagar.
Fri frakt inom Sverige för privatpersoner.
Finns även som
Visa alla 1 format & utgåvor
The three volume proceedings LNAI 11051 - 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
Visa hela texten

Passar bra ihop

  1. Machine Learning and Knowledge Discovery in Databases
  2. +
  3. Stealing the Network: How to Own an Identity

De som köpt den här boken har ofta också köpt Stealing the Network: How to Own an Identity av Ryan Russell, Riley Eller, Jay Beale, Chris Hurley, Tom Parker (häftad).

Köp båda 2 för 1478 kr


Har du läst boken? Sätt ditt betyg »


Graphs.- Temporally Evolving Community Detection and Prediction in Content-Centric Networks.- Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies.- Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery.- Dynamic hierarchies in temporal directed networks.- Risk-Averse Matchings over Uncertain Graph Databases.- Discovering Urban Travel Demands through Dynamic Zone Correlation in Location-Based Social Networks.- Social-Affiliation Networks: Patterns and the SOAR Model.- ONE-M: Modeling the Co-evolution of Opinions and Network Connections.- Think before You Discard: Accurate Triangle Counting in Graph Streams with Deletions.- Semi-Supervised Blockmodelling with Pairwise Guidance.- Kernel Methods.- Large-scale Nonlinear Variable Selection via Kernel Random Features.- Fast and Provably Effective Multi-view Classification with Landmark-based SVM.- Nystroem-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent.- Learning Paradigms.- Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds.- Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations.- VC-Dimension Based Generalization Bounds for Relational Learning.- Robust Super-Level Set Estimation using Gaussian Processes.- Robust Super-Level Set Estimation using Gaussian Processes.- Scalable Nonlinear AUC Maximization Methods.- Matrix and Tensor Analysis.- Lambert Matrix Factorization.- Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition.- MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds.- Block CUR: Decomposing Matrices using Groups of Columns.- Online and Active Learning.- SpectralLeader: Online Spectral Learning for Single Topic Models.- Online Learning of Weighted Relational Rules for Complex Event Recognition.- Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.- Online Feature Selection by Adaptive Sub-gradient Methods.- Frame-based Optimal Design.- Hierarchical Active Learning with Proportion Feedback on Regions.- Pattern and Sequence Mining.- An Efficient Algorithm for Computing Entropic Measures of Feature Subsets.- Anytime Subgroup Discovery in Numerical Domains with Guarantees.- Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics.- Mining Periodic Patterns with a MDL Criterion.- Revisiting Conditional Functional Dependency Discovery: Splitting the "C" from the "FD".- Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint.- Mining Tree Patterns with Partially Injective Homomorphisms.- Probabilistic Models and Statistical Methods.- Variational Bayes for Mixture Models with Censored Data.- Exploration Enhanced Expected Improvement for Bayesian Optimization.- A Left-to-right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis.- Causal Inference on Multivariate and Mixed-Type Data.- Recommender Systems.- POLAR: Attention-based CNN for One-shot Personalized Article Recommendation.- Learning Multi-granularity Dynamic Network Representations for Social Recommendation.- GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-based Social Networks.- Personalized Thread Recommendation for MOOC Discussion Forums.- Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation.- Transfer Learning.- Feature Selection for Unsupervised Domain Adaptation using Optimal Transport.- Towards more Reliable Transfer Learning.- Differentially Private Hypothesis Transfer Learning.- Information-theoretic Transfer Learning framework for Bayesian Optimisation.- A Unified Framework for Domain Adaptation using Metric Learning on Manifolds.