default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID19th PKDD / 26th ECML 2015: Porto, Portugal
- Annalisa Appice, Pedro Pereira Rodrigues
, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I. Lecture Notes in Computer Science 9284, Springer 2015, ISBN 978-3-319-23527-1
Research Track - Classification, Regression and Supervised Learning
- Vikas C. Raykar, Amrita Saha:
Data Split Strategiesfor Evolving Predictive Models. 3-19 - Rana Haber, Anand Rangarajan, Adrian M. Peter:
Discriminative Interpolation for Classification of Functional Data. 20-36 - Paul Mineiro, Nikos Karampatziakis:
Fast Label Embeddings via Randomized Linear Algebra. 37-51 - Wojciech Marian Czarnecki, Rafal Józefowicz, Jacek Tabor:
Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation. 52-67 - Meelis Kull, Peter A. Flach:
Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration. 68-85 - Sebastian Tschiatschek, Franz Pernkopf:
Parameter Learning of Bayesian Network Classifiers Under Computational Constraints. 86-101 - Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz:
Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning. 102-118 - Wendelin Böhmer, Klaus Obermayer:
Regression with Linear Factored Functions. 119-134 - Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, Yuji Matsumoto:
Ridge Regression, Hubness, and Zero-Shot Learning. 135-151 - Michael Großhans, Tobias Scheffer:
Solving Prediction Games with Parallel Batch Gradient Descent. 152-167 - Martin Ratajczak, Sebastian Tschiatschek, Franz Pernkopf:
Structured Regularizer for Neural Higher-Order Sequence Models. 168-183 - Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach:
Versatile Decision Trees for Learning Over Multiple Contexts. 184-199 - Andrea Dal Pozzolo, Olivier Caelen, Gianluca Bontempi:
When is Undersampling Effective in Unbalanced Classification Tasks? 200-215
Clustering and Unsupervised Learning
- Ehsan Amid, Aristides Gionis, Antti Ukkonen:
A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons. 219-234 - Yuanli Pei, Li-Ping Liu, Xiaoli Z. Fern:
Bayesian Active Clustering with Pairwise Constraints. 235-250 - Markus Ring, Florian Otto, Martin Becker, Thomas Niebler, Dieter Landes, Andreas Hotho:
ConDist: A Context-Driven Categorical Distance Measure. 251-266 - Junting Ye, Leman Akoglu:
Discovering Opinion Spammer Groups by Network Footprints. 267-282 - Ayan Acharya, Dean Teffer, Jette Henderson, Marcus Tyler, Mingyuan Zhou, Joydeep Ghosh:
Gamma Process Poisson Factorization for Joint Modeling of Network and Documents. 283-299 - Karim T. Abou-Moustafa, Dale Schuurmans:
Generalization in Unsupervised Learning. 300-317 - Weixiang Shao, Lifang He, Philip S. Yu:
Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization. 318-334 - Steven D. Prestwich, Adejuyigbe O. Fajemisin, Laura Climent, Barry O'Sullivan:
Solving a Hard Cutting Stock Problem by Machine Learning and Optimisation. 335-347
Data Preprocessing
- Konstantinos Sechidis, Gavin Brown:
Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data. 351-366 - Peng Luo, Jinye Peng, Ziyu Guan, Jianping Fan:
Multi-view Semantic Learning for Data Representation. 367-382 - Sen Wang, Feiping Nie, Xiaojun Chang, Lina Yao, Xue Li, Quan Z. Sheng:
Unsupervised Feature Analysis with Class Margin Optimization. 383-398
Data Streams and Online Learning
- Sebastian Wagner, Max Zimmermann, Eirini Ntoutsi, Myra Spiliopoulou:
Ageing-Based Multinomial Naive Bayes Classifiers Over Opinionated Data Streams. 401-416 - David Tse Jung Huang, Yun Sing Koh, Gillian Dobbie, Albert Bifet:
Drift Detection Using Stream Volatility. 417-432 - Asma Dachraoui, Alexis Bondu, Antoine Cornuéjols:
Early Classification of Time Series as a Non Myopic Sequential Decision Making Problem. 433-447 - Shaona Ghosh, Adam Prügel-Bennett:
Ising Bandits with Side Information. 448-463 - Yahel David, Nahum Shimkin:
Refined Algorithms for Infinitely Many-Armed Bandits with Deterministic Rewards. 464-479
Deep Learning
- Daniel Jiwoong Im, Ethan Buchman, Graham W. Taylor:
An Empirical Investigation of Minimum Probability Flow Learning Under Different Connectivity Patterns. 483-497 - Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Yoshua Bengio:
Difference Target Propagation. 498-515 - Alexander G. Ororbia II, David Reitter, Jian Wu, C. Lee Giles:
Online Learning of Deep Hybrid Architectures for Semi-supervised Categorization. 516-532 - Daniel Jiwoong Im, Graham W. Taylor:
Scoring and Classifying with Gated Auto-Encoders. 533-545 - Senjian An, Qiuhong Ke, Mohammed Bennamoun, Farid Boussaïd, Ferdous Ahmed Sohel:
Sign Constrained Rectifier Networks with Applications to Pattern Decompositions. 546-559 - Amos J. Storkey, Zhanxing Zhu, Jinli Hu:
Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets. 560-574
Distance and Metric Learning
- Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata:
Higher Order Fused Regularization for Supervised Learning with Grouped Parameters. 577-593 - Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban:
Joint Semi-supervised Similarity Learning for Linear Classification. 594-609 - Pengtao Xie:
Learning Compact and Effective Distance Metrics with Diversity Regularization. 610-624 - Farzaneh Mirzazadeh, Martha White, András György, Dale Schuurmans:
Scalable Metric Learning for Co-Embedding. 625-642
Large Scale Learning and Big Data
- Zhanxing Zhu, Amos J. Storkey:
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. 645-658 - Yinjie Huang, Michael Georgiopoulos, Georgios C. Anagnostopoulos:
Hash Function Learning via Codewords. 659-674 - Anveshi Charuvaka, Huzefa Rangwala:
HierCost: Improving Large Scale Hierarchical Classification with Cost Sensitive Learning. 675-690 - Ziqiang Shi, Rujie Liu:
Large Scale Optimization with Proximal Stochastic Newton-Type Gradient Descent. 691-704
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-09-26 23:29 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
