Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I
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| Format | eBook |
| Language | English |
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Springer International Publishing AG
2017
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| Edition | 1 |
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| Author | Vens, Celine Hollmén, Jaakko Dzeroski, Saso Todorovski, Ljupčo Ceci, Michelangelo |
|---|---|
| Author_xml | – sequence: 1 fullname: Ceci, Michelangelo – sequence: 2 fullname: Hollmén, Jaakko – sequence: 3 fullname: Todorovski, Ljupčo – sequence: 4 fullname: Vens, Celine – sequence: 5 fullname: Dzeroski, Saso |
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| Subtitle | European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I |
| TableOfContents | Intro -- Preface -- Organization -- Invited Talks Abstracts -- Towards End-to-End Learning and Optimization -- Frontiers in Recurrent Neural Network Research -- Using Networks to Link Genotype to Phenotype -- Multi-target Prediction via Low-Rank Embeddings -- Enabling a Smarter Planet with Earth Observation -- Automatic Understanding of the Visual World -- Abstracts of Journal Track Articles -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Anomaly Detection -- Concentration Free Outlier Detection -- 1 Introduction -- 2 The Concentration Free Outlier Factor -- 2.1 Definition -- 2.2 Relationship with the Distance Concentration Phenomenon -- 2.3 Relationship with the Hubness Phenomenon -- 2.4 Concentration Free Property of CFOF -- 3 Score Computation -- 3.1 The fast-CFOF Technique -- 4 Experimental Results -- 4.1 Accuracy -- 4.2 Scalability -- 4.3 Effectiveness -- 4.4 Comparison with Other Approaches -- 5 Conclusions -- References -- Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection -- 1 Introduction -- 2 Evaluation Criteria and Related Work -- 3 Stochastic Gradient Boosting with AP -- 3.1 Stochastic Gradient Boosting -- 3.2 Sigmoid-Based Surrogate of AP -- 3.3 Exponential-Based Surrogate of AP -- 3.4 Comparison Between the Approximations of AP -- 4 Experiments -- 4.1 Top-Rank Quality over Unbalanced Datasets -- 4.2 Top Rank Capability for a Decreasing Positive Ratio -- 5 Conclusion and Perspectives -- References -- Robust, Deep and Inductive Anomaly Detection -- 1 Anomaly Detection: Motivation and Challenges -- 2 Background and Related Work on Anomaly Detection -- 2.1 A Tour of Anomaly Detection Methods -- 2.2 PCA for Anomaly Detection -- 2.3 Autoencoders for Anomaly Detection -- 2.4 Robust PCA -- 2.5 Direct Robust Matrix Factorization -- 2.6 Robust Kernel PCA Computer Vision -- Alternative Semantic Representations for Zero-Shot Human Action Recognition -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Text-Based Semantic Representation -- 3.2 Image-Based Semantic Representation -- 4 Experimental Settings -- 4.1 Dataset -- 4.2 Zero-Shot Recognition Method -- 4.3 Video Representation -- 4.4 Evaluation -- 5 Experimental Results -- 5.1 Text-Based Representation -- 5.2 Image-Based Representation -- 5.3 Comparison with Other Semantic Representations -- 5.4 How Many Images Are Enough? -- 6 Conclusions and Future Work -- References -- Early Active Learning with Pairwise Constraint for Person Re-identification -- 1 Introduction -- 2 The Proposed Framework -- 2.1 Early Active Learning -- 2.2 Early Active Learning with Pairwise Constraint -- 3 Optimization -- 4 Convergence Analysis -- 5 Experimental Study -- 5.1 Datasets and Settings -- 5.2 Experimental Result Analysis -- 6 Conclusion -- References -- Guiding InfoGAN with Semi-supervision -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries: GAN and InfoGAN -- 3.2 Semi-supervised InfoGAN -- 4 Implementation -- 5 Experiments -- 5.1 MNIST -- 5.2 SVHN -- 5.3 CelebA -- 5.4 CIFAR-10 -- 5.5 Convergence Speed of Sample Quality -- 6 Conclusion -- References -- Scatteract: Automated Extraction of Data from Scatter Plots -- 1 Introduction -- 2 Datasets -- 2.1 Procedurally Generated Scatter Plots -- 2.2 Scatter Plots from the Web -- 3 Methodology -- 3.1 Object Detection -- 3.2 Optical Character Recognition -- 3.3 Axis Splitting -- 3.4 RANSAC Regression -- 4 Results -- 4.1 Performance Analysis -- 4.2 Error Analysis -- 5 Conclusion -- References -- Unsupervised Diverse Colorization via Generative Adversarial Networks -- 1 Introduction -- 2 Related Work -- 2.1 Diverse Colorization -- 2.2 Conditional GAN -- 3 Methods -- 3.1 Problem Formulation 2.2 Architecture of Our Network -- 2.3 Objective Function -- 2.4 Learning -- 2.5 Out-of-Sample Extension -- 3 Experiment -- 3.1 Datasets -- 3.2 Results on Image Retrieval -- 3.3 Results on Object Recognition -- 4 Conclusion and Future Work -- References -- Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets -- 1 Introduction -- 2 Related Work -- 3 Problem Overview -- 4 Relevance and Redundancy Ranking (RaR) -- 4.1 Subspace Relevance -- 4.2 Decomposition for Feature Relevance Estimation -- 4.3 Redundancy Estimation -- 4.4 RaR: Relevance and Redundancy Scoring -- 4.5 Instantiations for RaR -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Synthetic Data -- 5.3 Parameter Analysis -- 5.4 Robustness w.r.t. Erroneous Labels -- 5.5 Real World Datasets -- 5.6 Evaluation of the Ranking -- 6 Conclusions and Future Works -- References -- Non-redundant Spectral Dimensionality Reduction -- 1 Introduction -- 2 Related Work -- 3 Eliminating Redundancy -- 4 Algorithm -- 4.1 Relation to Independent Component Analysis (ICA) -- 5 Experiments -- 5.1 Artificial Head Images -- 5.2 Image Patch Representation -- 5.3 MNIST Handwritten Digits -- 6 Conclusions -- A A Proof of Lemma 2 -- References -- Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-Links -- 1 Introduction -- 2 Related Work -- 3 Formulations -- 3.1 Notations -- 3.2 Discriminatively Exploiting Similarity -- 4 Instantiations of the DES -- 4.1 Hypothesis Test Based DES (HT-DES) -- 4.2 Classification-Based DES (CL-DES) -- 5 Optimization -- 6 Experiment -- 6.1 Baselines -- 6.2 Datasets -- 6.3 Experimental Setting -- 6.4 Clustering Results -- 6.5 Sensitivity Analysis -- 7 Conclusion -- References -- SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble -- 1 Introduction -- 2 Related Work 3.2 Architecture and Implementation Details -- 3.3 Training and Testing Procedure -- 4 Experiments -- 4.1 Dataset -- 4.2 Comparison Experiments -- 5 Results and Evaluation -- 5.1 Colorization Results -- 5.2 Evaluation via Human Study -- 6 Conclusion -- References -- Ensembles and Meta Learning -- Dynamic Ensemble Selection with Probabilistic Classifier Chains -- 1 Introduction -- 2 Problem Statement and Contribution -- 2.1 Dynamic Ensemble Selection (DES) -- 2.2 DES as a Multi-label Classification Problem -- 2.3 DES Loss Function -- 2.4 MLC Approaches to the DES Problem -- 2.5 Probabilistic Classifier Chains and Monte Carlo Inference -- 3 Experiments -- 3.1 Ensemble Generation -- 3.2 Compared Methods and Evaluation Protocol -- 3.3 Results and Discussion -- 4 Conclusion -- References -- Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks -- 1 Introduction -- 2 Preliminary: Parallel Training of DNN -- 3 Model Aggregation: MA vs. Ensemble -- 4 EC-DNN -- 4.1 Framework -- 4.2 Implementations -- 4.3 Time Complexity -- 4.4 Comparison with Traditional Ensemble Methods -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Compared Methods -- 5.3 Experimental Results -- 6 Conclusion and Future Work -- References -- Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks -- 1 Introduction -- 2 Background -- 2.1 Product of Bernoulli Distributions -- 2.2 Probabilistic Tree Models -- 3 Cutset Networks -- 3.1 Learning CNets -- 3.2 Learning Ensembles of CNets -- 4 Extremely Randomized CNets -- 5 Experiments -- 5.1 (Q1) Single Model Performances -- 5.2 (Q2) Ensemble Performances -- 5.3 (Q3) Running Times -- 6 Conclusions -- References -- Feature Selection and Extraction -- Deep Discrete Hashing with Self-supervised Pairwise Labels -- 1 Introduction -- 2 Our Method -- 2.1 Construction of Pairwise Labels 3 Our Methodology: The SetExpan Framework 3 From Robust PCA to Robust Autoencoders -- 3.1 Robust (Convolutional) Autoencoders -- 3.2 Training the Model -- 3.3 Predicting with the Model -- 3.4 Connection to Robust PCA -- 3.5 Relation to Existing Models -- 4 Experimental Setup -- 4.1 Methods Compared -- 4.2 Datasets -- 4.3 Evaluation Methodology -- 4.4 Network Parameters -- 5 Experimental Results -- 5.1 Non-inductive Anomaly Detection Results -- 5.2 Inductive Anomaly Detection Results -- 5.3 Image Denoising Results -- 5.4 Comparison of Training Times -- 6 Conclusion -- References -- Sentiment Informed Cyberbullying Detection in Social Media -- 1 Introduction -- 2 Problem Definition -- 3 Exploratory Data Analysis -- 3.1 Datasets -- 3.2 Verifying the Sentiment Score Distribution Difference -- 3.3 Verifying Sentiment Consistency -- 4 The Proposed Framework - SICD -- 4.1 Modeling Content of Social Media Posts -- 4.2 Modeling User-Post Relationships -- 4.3 Modeling Sentiment Information -- 4.4 Sentiment Informed Cyberbullying Detection (SICD) -- 5 Algorithmic Details -- 5.1 Optimization Algorithm for SICD -- 5.2 Time Complexity Analysis -- 6 Experiments -- 6.1 Experimental Settings -- 6.2 Performance Evaluation -- 6.3 Impact of Sentiment Information -- 6.4 Parameter Sensitivity -- 7 Related Work -- 8 Conclusion and Future Work -- References -- ZOORANK: Ranking Suspicious Entities in Time-Evolving Tensors -- 1 Introduction -- 2 Background and Related Work -- 3 Preliminaries and Problem Definition -- 3.1 Problem Definition -- 3.2 Block Level Suspiciousness Metrics -- 3.3 Axioms -- 3.4 Shortcomings of Other Metrics -- 4 Proposed Approach: ZOORANK -- 4.1 Temporal Feature Handling -- 4.2 Proposed Metric -- 4.3 Algorithm -- 5 Experiments -- 5.1 Datasets -- 5.2 Q1. Effectiveness of ZOORANK -- 5.3 Q2. Generalizability of ZOORANK -- 5.4 Q3. Scalability of ZOORANK -- 6 Conclusions -- References |
| Title | Machine Learning and Knowledge Discovery in Databases |
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