Machine Learning and Knowledge Discovery in Databases - neues Buch

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ISBN/EAN: 9783319712499

ISBN - alternative Schreibweisen:
978-3-319-71249-9
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: celine, michelangelo, céline
Titel des Buches: machine learning

Daten vom Verlag:

Autor/in: Michelangelo Ceci; Jaakko Hollmén; Ljupčo Todorovski; Celine Vens; Sašo Džeroski
Titel: Lecture Notes in Artificial Intelligence; Lecture Notes in Computer Science; Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I
Verlag: Springer; Springer International Publishing
852 Seiten
Erscheinungsjahr: 2017-12-29
Cham; CH
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
LXIII, 852 p. 245 illus.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Data Mining; Verstehen; anomaly detection; artificial intelligence; Bayesian networks; classification; clustering algorithms; data mining; data security; data stream; image processing; Kernel method; learning algorithm; machine learning; neural networks; recommender systems; reinforcement learning; signal processing; social networking; supervised learning; Support Vector Machines (SVM); world wide web; C; Data Mining and Knowledge Discovery; Artificial Intelligence; Computer Vision; Computer and Information Systems Applications; Data and Information Security; Computing Milieux; Computer Science; Wissensbasierte Systeme, Expertensysteme; Künstliche Intelligenz; Maschinelles Sehen, Bildverstehen; Angewandte Informatik; Computersicherheit; Netzwerksicherheit; Informationstechnik (IT), allgemeine Themen; BC

The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. 

Part III: applied data science track; nectar track; and demo track.

Anomaly Detection.- Concentration Free Outlier Detection.- Efficient top rank optimization with gradient boosting for supervised anomaly detection.- Robust, Deep and Inductive Anomaly Detection.- Sentiment Informed Cyberbullying Detection in Social Media.- zooRank: Ranking Suspicious Activities in Time-Evolving Tensors.- Computer Vision.- Alternative Semantic Representations for Zero-Shot Human Action Recognition.- Early Active Learning with Pairwise Constraint for Person Re-identification.- Guiding InfoGAN with Semi-Supervision.- Scatteract: Automated extraction of data from scatter plots.- Unsupervised Diverse Colorization via Generative Adversarial Networks.- Ensembles and Meta Learning.- Dynamic Ensemble Selection with Probabilistic Classifier Chains.- Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks.- Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks.- Feature Selection and Extraction.- Deep Discrete Hashing with Self-supervised Labels.- Including multi-feature interactions and redundancy for feature ranking in mixed datasets.- Non-redundant Spectral Dimensionality Reduction.- Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-links.- SetExpan: Corpus-based Set Expansion via Context Feature Selection and Rank Ensemble.- Kernel Methods.- Bayesian Nonlinear Support Vector Machines for Big Data.- Entropic Trace Estimation for Log Determinants.- Fair Kernel Learning.- GaKCo: a Fast Gapped k-mer string Kernel using Counting.- Graph Enhanced Memory Networks for Sentiment Analysis.- Kernel Sequential Monte Carlo.- Learning Lukasiewicz Logic Fragments by Quadratic Programming.- Nystrom sketching.- Learning and Optimization.- Crossprop: learning representations by stochastic meta-gradient descent in neural networks.- Distributed Stochastic Optimization of the Regularized Risk via Saddle-point Problem.- Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves using Previous Builds.- Matrix and Tensor Factorization.- Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation.- Content-Based Social Recommendation with Poisson Matrix Factorization.- C-SALT: Mining Class-Speci_c ALTerations in Boolean Matrix Factorization.- Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition.- Structurally Regularized Non-negative Tensor Factorization for Spatio-temporal Pattern Discoveries.- Networks and Graphs.- Attributed Graph Clustering with Unimodal Normalized Cut.- K-clique-graphs for Dense Subgraph Discovery.- Learning and Scaling Directed Networks via Graph Embedding.- Local Lanczos Spectral Approximation for Membership Identification.- Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms.- Survival Factorization for Topical Cascades on Diffusion Networks.- The network-untangling problem: From interactions to activity timelines.-TransT: Type-based Multiple Embedding Representations forKnowledge Graph Completion.- Neural Networks and Deep Learning.- A network Architecture for Multi-multi Instance Learning.- CON-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec.- Deep Over-sampling Framework for Classifying Imbalanced Data.- FCNNs: Fourier Convolutional Neural Networks.- Joint User Modeling across Aligned Heterogeneous Sites using Neural Networks.- Sequence Generation with Target Attention.- Wikipedia Vandal Early Detection: from User Behavior to User Embedding.