Cantitate/Preț
Produs
Total produse
Vezi coșul
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II de Michelangelo Ceci

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II: Lecture Notes in Computer Science, cartea 10535

Editat de Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski
en Limba Engleză Paperback – 10 ian 2018
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. 
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.  The contributions were organized in topical sections named as follows:
Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.
Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.
Part III: applied data science track; nectar track; and demo track.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (2) 34861 lei  6-8 săpt.
  Springer International Publishing – 10 ian 2018 34861 lei  6-8 săpt.
  Springer International Publishing – 30 dec 2017 34942 lei  6-8 săpt.

Din seria Lecture Notes in Computer Science

Preț: 34861 lei

Preț vechi: 43576 lei
-20% Nou

Puncte Express: 523
Preț estimativ în valută:
6672€ • 7038$ • 5576£

Carte tipărită la comandă

Livrare economică 01-15 ianuarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319712451
ISBN-10: 3319712454
Pagini: 821
Ilustrații: XXXIII, 866 p. 213 illus.
Dimensiuni: 155 x 235 mm
Greutate: 1.24 kg
Ediția:1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Pattern and Sequence Mining.- BeatLex: Summarizing and Forecasting Time Series with Patterns.- Behavioral Constraint Template-Based Sequence Classification.- Efficient Sequence Regression by Learning Linear Models in All-Subsequence Space.- Subjectively Interesting Connecting Trees.- Privacy and Security.- Malware Detection by Analysing Encrypted Network Traffic with Neural Networks.- PEM: Practical Differentially Private System for Large-Scale Cross-Institutional Data Mining.- Probabilistic Models and Methods.- Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources.- Bayesian Inference for Least Squares Temporal Difference Regularization.- Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints.- Labeled DBN learning with community structure knowledge.- Multi-view Generative Adversarial Networks.- Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models.- PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach.- Partial Device Fingerprints.- Robust Multi-view Topic Modeling by Incorporating Detecting Anomalies.- Recommendation.- A Regularization Method with Inference of Trust and Distrust in Recommender
Systems.- A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations.- Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation.- Regression.- Adaptive Skip-Train Structured Regression for Temporal Networks.- ALADIN: A New Approach for Drug-Target Interaction Prediction.- Co-Regularised Support Vector Regression.- Online Regression with Controlled Label Noise Rate.- Reinforcement Learning.- Generalized Inverse Reinforcement Learning with Linearly Solvable MDP.- Max K-armed bandit: On the ExtremeHunter algorithm and beyond.- Variational Thompson Sampling for Relational Recurrent Bandits.- Subgroup Discovery.- Explaining Deviating Subsetsthrough Explanation Networks.- Flash points: Discovering exceptional pairwise behaviors in vote or rating data.- Time Series and Streams.- A Multiscale Bezier-Representation for Time Series that Supports Elastic Matching.- Arbitrated Ensemble for Time Series Forecasting.- Cost Sensitive Time-series Classification.- Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams.- Efficient Temporal Kernels between Feature Sets for Time Series Classification.- Forecasting and Granger modelling with non-linear dynamical dependencies.- Learning TSK Fuzzy Rules from Data Streams.- Non-Parametric Online AUC Maximization.- On-line Dynamic Time Warping for Streaming Time Series.- PowerCast: Mining and Forecasting Power Grid Sequences.- UAPD: Predicting Urban Anomalies from Spatial-Temporal Data.- Transfer and Multi-Task Learning.- A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering.- Distributed Multi-task Learning for SensorNetwork.- Learning task structure via sparsity grouped multitask learning.- Lifelong Learning with Gaussian Processes.- Personalized Tag Recommendation for Images Using Deep Transfer Learning.- Ranking based Multitask Learning of Scoring Functions.- Theoretical Analysis of Domain Adaptation with Optimal Transport.- TSP: Learning Task-Speci_c Pivots for Unsupervised Domain Adaptation.- Unsupervised and Semisupervised Learning.- k2-means for fast and accurate large scale clustering.- A Simple Exponential Family Framework for Zero-Shot Learning.- DeepCluster: A General Clustering Framework based on Deep Learning.- Multi-view Spectral Clustering on Conflicting Views.- Pivot-based Distributed K-Nearest Neighbor Mining.

Caracteristici

Includes supplementary material: sn.pub/extras