國立高雄大學圖資館 |

Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings.Part I /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and knowledge discovery in databasesedited by Michele Berlingerio ... [et al.].
Reminder of title:	European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings.
remainder title:	ECML PKDD 2018
other author:	Berlingerio, Michele.
corporate name:
Published:	Cham :Springer International Publishing :2019.
Description:	xxxviii, 740 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learningCongresses.
Online resource:	https://doi.org/10.1007/978-3-030-10925-7
ISBN:	9783030109257$q(electronic bk.)

Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings.Part I /
Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings.Part I /[electronic resource] :ECML PKDD 2018edited by Michele Berlingerio ... [et al.]. - Cham :Springer International Publishing :2019. - xxxviii, 740 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,110510302-9743 ;. - Lecture notes in computer science ;4891..

Adversarial Learning -- Image Anomaly Detection with Generative Adversarial Networks -- Image-to-Markup Generation via Paired Adversarial Learning -- Toward an Understanding of Adversarial Examples in Clinical Trials -- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector -- Anomaly and Outlier Detection -- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid -- Incorporating Privileged Information to Unsupervised Anomaly Detection -- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space -- Beyond Outlier Detection: LookOut for Pictorial Explanation -- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features -- Group Anomaly Detection using Deep Generative Models -- Applications -- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements -- Face-Cap: Image Captioning using Facial Expression Analysis -- Pedestrian Trajectory Prediction with Structured Memory Hierarchies -- Classification -- Multiple Instance Learning with Bag-level Randomized Trees -- One-class Quantification -- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study -- Ordinal Label Proportions -- AWX: An Integrated Approach to Hierarchical-Multilabel Classification -- Clustering and Unsupervised Learning -- Clustering in the Presence of Concept Drift -- Time Warp Invariant Dictionary Learning for Time Series Clustering -- How Your Supporters and Opponents Define Your Interestingness -- Deep Learning -- Efficient Decentralized Deep Learning by Dynamic Model Averaging -- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems -- Towards Efficient Forward Propagation on Resource-Constrained Systems -- Auxiliary Guided Autoregressive Variational Autoencoders -- Cooperative Multi-Agent Policy Gradient -- Parametric t-Distributed Stochastic Exemplar-centered Embedding -- Joint autoencoders: a flexible meta-learning framework -- Privacy Preserving Synthetic Data Release Using Deep Learning -- On Finer Control of Information Flow in LSTMs -- MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes -- Ontology alignment based on word embedding and random forest classification -- Domain Adaption in One-Shot Learning -- Ensemble Methods -- Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure -- Modular Dimensionality Reduction -- Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles -- MetaBags: Bagged Meta-Decision Trees for Regression -- Evaluation -- Visualizing the Feature Importance for Black Box Models -- Efficient estimation of AUC in a sliding window -- Controlling and visualizing the precision-recall tradeoff for external performance indices -- Evaluation Procedures for Forecasting with Spatio-Temporal Data -- A Blended Metric for Multi-label Optimisation and Evaluation.

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.

ISBN: 9783030109257$q(electronic bk.)

Standard No.: 10.1007/978-3-030-10925-7doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning and knowledge discovery in databasesEuropean Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings.Part I /
LDR:05325nmm a2200361 a 4500 001 555271
003 DE-He213
005 20190119021417.0
006 m d
007 cr nn 008maaau
008 191121s2019 gw s 0 eng d
020 $a 9783030109257$q(electronic bk.)
020 $a 9783030109240$q(paper)
024 7 $a 10.1007/978-3-030-10925-7 $2 doi
035 $a 978-3-030-10925-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .E19 2018
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Machine learning and knowledge discovery in databases $h [electronic resource] : $b European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings. $n Part I / $c edited by Michele Berlingerio ... [et al.].
246 3 $a ECML PKDD 2018
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xxxviii, 740 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11051
490 1 $a Lecture notes in artificial intelligence
505 0 $a Adversarial Learning -- Image Anomaly Detection with Generative Adversarial Networks -- Image-to-Markup Generation via Paired Adversarial Learning -- Toward an Understanding of Adversarial Examples in Clinical Trials -- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector -- Anomaly and Outlier Detection -- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid -- Incorporating Privileged Information to Unsupervised Anomaly Detection -- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space -- Beyond Outlier Detection: LookOut for Pictorial Explanation -- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features -- Group Anomaly Detection using Deep Generative Models -- Applications -- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements -- Face-Cap: Image Captioning using Facial Expression Analysis -- Pedestrian Trajectory Prediction with Structured Memory Hierarchies -- Classification -- Multiple Instance Learning with Bag-level Randomized Trees -- One-class Quantification -- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study -- Ordinal Label Proportions -- AWX: An Integrated Approach to Hierarchical-Multilabel Classification -- Clustering and Unsupervised Learning -- Clustering in the Presence of Concept Drift -- Time Warp Invariant Dictionary Learning for Time Series Clustering -- How Your Supporters and Opponents Define Your Interestingness -- Deep Learning -- Efficient Decentralized Deep Learning by Dynamic Model Averaging -- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems -- Towards Efficient Forward Propagation on Resource-Constrained Systems -- Auxiliary Guided Autoregressive Variational Autoencoders -- Cooperative Multi-Agent Policy Gradient -- Parametric t-Distributed Stochastic Exemplar-centered Embedding -- Joint autoencoders: a flexible meta-learning framework -- Privacy Preserving Synthetic Data Release Using Deep Learning -- On Finer Control of Information Flow in LSTMs -- MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes -- Ontology alignment based on word embedding and random forest classification -- Domain Adaption in One-Shot Learning -- Ensemble Methods -- Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure -- Modular Dimensionality Reduction -- Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles -- MetaBags: Bagged Meta-Decision Trees for Regression -- Evaluation -- Visualizing the Feature Importance for Black Box Models -- Efficient estimation of AUC in a sliding window -- Controlling and visualizing the precision-recall tradeoff for external performance indices -- Evaluation Procedures for Forecasting with Spatio-Temporal Data -- A Blended Metric for Multi-label Optimisation and Evaluation.
520 $a 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.
650 0 $a Machine learning $v Congresses. $3 384498
650 0 $a Data mining $v Congresses. $3 380776
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 274376
650 2 4 $a Computing Milieux. $3 275270
650 2 4 $a Security. $3 760527
700 1 $a Berlingerio, Michele. $3 837320
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Lecture notes in artificial intelligence. $3 822012
856 4 0 $u https://doi.org/10.1007/978-3-030-10925-7
950 $a Computer Science (Springer-11645)