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ORCID21st PKDD / 28th ECML 2017: Skopje, Macedonia
- Michelangelo Ceci, Jaakko Hollmén, Ljupco Todorovski, Celine Vens, Saso Dzeroski:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I. Lecture Notes in Computer Science 10534, Springer 2017, ISBN 978-3-319-71248-2
Anomaly Detection
- Fabrizio Angiulli
:
Concentration Free Outlier Detection. 3-19 - Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton:
Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection. 20-35 - Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla:
Robust, Deep and Inductive Anomaly Detection. 36-51 - Harsh Dani, Jundong Li, Huan Liu:
Sentiment Informed Cyberbullying Detection in Social Media. 52-67 - Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, Jürgen Pfeffer:
zooRank: Ranking Suspicious Entities in Time-Evolving Tensors. 68-84
Computer Vision
- Qian Wang, Ke Chen:
Alternative Semantic Representations for Zero-Shot Human Action Recognition. 87-102 - Wenhe Liu, Xiaojun Chang, Ling Chen, Yi Yang:
Early Active Learning with Pairwise Constraint for Person Re-identification. 103-118 - Adrian Spurr, Emre Aksan, Otmar Hilliges:
Guiding InfoGAN with Semi-supervision. 119-134 - Mathieu Cliche, David S. Rosenberg, Dhruv Madeka, Connie Yee:
Scatteract: Automated Extraction of Data from Scatter Plots. 135-150 - Yun Cao, Zhiming Zhou, Weinan Zhang, Yong Yu:
Unsupervised Diverse Colorization via Generative Adversarial Networks. 151-166
Ensembles and Meta Learning
- Anil Narassiguin, Haytham Elghazel, Alex Aussem:
Dynamic Ensemble Selection with Probabilistic Classifier Chains. 169-186 - Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks. 187-202 - Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Floriana Esposito:
Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks. 203-219
Feature Selection and Extraction
- Jingkuan Song, Tao He, Hangbo Fan, Lianli Gao:
Deep Discrete Hashing with Self-supervised Pairwise Labels. 223-238 - Arvind Kumar Shekar, Tom Bocklisch, Patricia Iglesias Sánchez, Christoph Nikolas Straehle, Emmanuel Müller:
Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets. 239-255 - Yochai Blau, Tomer Michaeli:
Non-redundant Spectral Dimensionality Reduction. 256-271 - Xiaokai Wei, Sihong Xie, Bokai Cao, Philip S. Yu:
Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-Links. 272-287 - Jiaming Shen, Zeqiu Wu, Dongming Lei, Jingbo Shang, Xiang Ren, Jiawei Han:
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble. 288-304
Kernel Methods
- Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch, Marius Kloft:
Bayesian Nonlinear Support Vector Machines for Big Data. 307-322 - Jack K. Fitzsimons, Diego Granziol, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone, Stephen J. Roberts:
Entropic Trace Estimates for Log Determinants. 323-338 - Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-Garcia, Jordi Muñoz-Marí, Luis Gómez-Chova, Gustau Camps-Valls:
Fair Kernel Learning. 339-355 - Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, Yanjun Qi:
GaKCo: A Fast Gapped k-mer String Kernel Using Counting. 356-373 - Zhao Xu, Romain Vial, Kristian Kersting:
Graph Enhanced Memory Networks for Sentiment Analysis. 374-389 - Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic:
Kernel Sequential Monte Carlo. 390-409 - Francesco Giannini, Michelangelo Diligenti, Marco Gori, Marco Maggini:
Learning Łukasiewicz Logic Fragments by Quadratic Programming. 410-426 - Daniel J. Perry, Braxton Osting, Ross T. Whitaker:
Nyström Sketches. 427-442
Learning and Optimization
- Vivek Veeriah, Shangtong Zhang, Richard S. Sutton:
Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. 445-459 - Shin Matsushima, Hyokun Yun, Xinhua Zhang, S. V. N. Vishwanathan:
Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem. 460-476 - Akshay Chandrashekaran, Ian R. Lane:
Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves Using Previous Builds. 477-492 - Tong Yu, Branislav Kveton, Ole J. Mengshoel:
Thompson Sampling for Optimizing Stochastic Local Search. 493-510
Matrix and Tensor Factorization
- Thomas Brouwer, Jes Frellsen, Pietro Liò:
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. 513-529 - Eliezer de Souza da Silva, Helge Langseth, Heri Ramampiaro:
Content-Based Social Recommendation with Poisson Matrix Factorization. 530-546 - Sibylle Hess, Katharina Morik:
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. 547-563 - Qiquan Shi, Yiu-ming Cheung, Qibin Zhao:
Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition. 564-581 - Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata:
Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries. 582-598
Networks and Graphs
- Wei Ye, Linfei Zhou, Xin Sun, Claudia Plant, Christian Böhm:
Attributed Graph Clustering with Unimodal Normalized Cut. 601-616 - Giannis Nikolentzos, Polykarpos Meladianos, Yannis Stavrakas, Michalis Vazirgiannis:
K-Clique-Graphs for Dense Subgraph Discovery. 617-633 - Mikhail Drobyshevskiy, Anton Korshunov, Denis Turdakov:
Learning and Scaling Directed Networks via Graph Embedding. 634-650 - Pan Shi, Kun He, David Bindel, John E. Hopcroft:
Local Lanczos Spectral Approximation for Community Detection. 651-667 - Pasquale Minervini, Luca Costabello, Emir Muñoz, Vít Novácek, Pierre-Yves Vandenbussche:
Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms. 668-683 - Nicola Barbieri, Giuseppe Manco, Ettore Ritacco:
Survival Factorization on Diffusion Networks. 684-700 - Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis:
The Network-Untangling Problem: From Interactions to Activity Timelines. 701-716 - Shiheng Ma, Jianhui Ding, Weijia Jia, Kun Wang, Minyi Guo:
TransT: Type-Based Multiple Embedding Representations for Knowledge Graph Completion. 717-733
Neural Networks and Deep Learning
- Alessandro Tibo, Paolo Frasconi, Manfred Jaeger:
A Network Architecture for Multi-Multi-Instance Learning. 737-752 - Tanay Kumar Saha, Shafiq R. Joty, Mohammad Al Hasan:
Con-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec. 753-769 - Shin Ando, Chun-Yuan Huang:
Deep Over-sampling Framework for Classifying Imbalanced Data. 770-785 - Harry Pratt, Bryan M. Williams, Frans Coenen, Yalin Zheng:
FCNN: Fourier Convolutional Neural Networks. 786-798 - Xuezhi Cao, Yong Yu:
Joint User Modeling Across Aligned Heterogeneous Sites Using Neural Networks. 799-815 - Yingce Xia, Fei Tian, Tao Qin, Nenghai Yu, Tie-Yan Liu:
Sequence Generation with Target Attention. 816-831 - Shuhan Yuan, Panpan Zheng, Xintao Wu, Yang Xiang:
Wikipedia Vandal Early Detection: From User Behavior to User Embedding. 832-846
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