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  • 1
    Book
    Book
    New York [u.a.] :Chapman and Hall,
    UID:
    almafu_BV010326964
    Format: XX, 391 S. : graph. Darst.
    Edition: 1. publ.
    ISBN: 0-412-03431-X
    Language: English
    Subjects: Economics , Mathematics
    RVK:
    RVK:
    Keywords: Statistische Prozesslenkung
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  • 2
    UID:
    b3kat_BV041889706
    Format: 1 Online-Ressource
    ISBN: 9783642051791
    Series Statement: Studies in Computational Intelligence 263
    Note: This is the second volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology. His research career started in the mid-1960s in Poland, in the Institute of Automation, Polish Academy of Sciences in Warsaw, Poland. He left for the USA in 1970, and since then had worked there at various universities, notably, at the University of Illinois at Urbana – Champaign and finally, until his untimely death, at George Mason University. We, the editors, had been lucky to be able to meet and collaborate with Ryszard for years, indeed some of us knew him when he was still in Poland. After he started working in the USA, he was a frequent visitor to Poland, taking part at many conferences until his death. , We had also witnessed with a great personal pleasure honors and awards he had received over the years, notably when some years ago he was elected Foreign Member of the Polish Academy of Sciences among some top scientists and scholars from all over the world, including Nobel prize winners. Professor Michalski's research results influenced very strongly the development of machine learning, data mining, and related areas. Also, he inspired many established and younger scholars and scientists all over the world. We feel very happy that so many top scientists from all over the world agreed to pay the last tribute to Professor Michalski by writing papers in their areas of research. These papers will constitute the most appropriate tribute to Professor Michalski, a devoted scholar and researcher. Moreover, we believe that they will inspire many newcomers and younger researchers in the area of broadly perceived machine learning, data analysis and data mining. , The papers included in the two volumes, Machine Learning I and Machine Learning II, cover diverse topics, and various aspects of the fields involved. For convenience of the potential readers, we will now briefly summarize the contents of the particular chapters , General Issues -- Knowledge-Oriented and Distributed Unsupervised Learning for Concept Elicitation -- Toward Interactive Computations: A Rough-Granular Approach -- Data Privacy: From Technology to Economics -- Adapting to Human Gamers Using Coevolution -- Wisdom of Crowds in the Prisoner's Dilemma Context -- Logical and Relational Learning, and Beyond -- Towards Multistrategic Statistical Relational Learning -- About Knowledge and Inference in Logical and Relational Learning -- Two Examples of Computational Creativity: ILP Multiple Predicate Synthesis and the 'Assets' in Theorem Proving -- Logical Aspects of the Measures of Interestingness of Association Rules -- Text and Web Mining -- Clustering the Web 2.0 -- Induction in Multi-Label Text Classification Domains -- Cluster-Lift Method for Mapping Research Activities over a Concept Tree -- On Concise Representations of Frequent Patterns Admitting Negation -- Classification and Beyond -- A System to Detect Inconsistencies between a Domain Expert's Different Perspectives on (Classification) Tasks -- The Dynamics of Multiagent Q-Learning in Commodity Market Resource Allocation -- Simple Algorithms for Frequent Item Set Mining -- Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification -- Machine Learning Methods in Automatic Image Annotation -- Neural Networks and Other Nature Inspired Approaches -- Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework -- Machine Learning in Vector Models of Neural Networks -- Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction -- Discovering Data Structures Using Meta-learning, Visualization and Constructive Neural Networks -- Neural Network and Artificial Immune Systems for Malware and Network Intrusion Detection -- Immunocomputing for Speaker Recognition
    Additional Edition: Erscheint auch als Druckausgabe ISBN 978-3-642-05178-4
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Festschrift
    Author information: Raś, Zbigniew W. 1947-
    Author information: Kacprzyk, Janusz 1947-
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  • 3
    UID:
    b3kat_BV041889705
    Format: 1 Online-Ressource (XX, 524p. 154 illus)
    ISBN: 9783642051777
    Series Statement: Studies in Computational Intelligence 262
    Note: This is the first volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology. His research career started in the mid-1960s in Poland, in the Institute of Automation, Polish Academy of Sciences in Warsaw, Poland. He left for the USA in 1970, and since then had worked there at various universities, notably, at the University of Illinois at Urbana – Champaign and finally, until his untimely death, at George Mason University. We, the editors, had been lucky to be able to meet and collaborate with Ryszard for years, indeed some of us knew him when he was still in Poland. After he started working in the USA, he was a frequent visitor to Poland, taking part at many conferences until his death. , We had also witnessed with a great personal pleasure honors and awards he had received over the years, notably when some years ago he was elected Foreign Member of the Polish Academy of Sciences among some top scientists and scholars from all over the world, including Nobel prize winners. Professor Michalski's research results influenced very strongly the development of machine learning, data mining, and related areas. Also, he inspired many established and younger scholars and scientists all over the world. We feel very happy that so many top scientists from all over the world agreed to pay the last tribute to Professor Michalski by writing papers in their areas of research. These papers will constitute the most appropriate tribute to Professor Michalski, a devoted scholar and researcher. Moreover, we believe that they will inspire many newcomers and younger researchers in the area of broadly perceived machine learning, data analysis and data mining. , The papers included in the two volumes, Machine Learning I and Machine Learning II, cover diverse topics, and various aspects of the fields involved. For convenience of the potential readers, we will now briefly summarize the contents of the particular chapters , Introductory Chapters -- Ryszard S. Michalski: The Vision and Evolution of Machine Learning -- The AQ Methods for Concept Drift -- Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski -- Inductive Learning: A Combinatorial Optimization Approach -- General Issues -- From Active to Proactive Learning Methods -- Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms -- Transfer Learning via Advice Taking -- Classification and Beyond -- Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning -- Transductive Learning for Spatial Data Classification -- Beyond Sequential Covering – Boosted Decision Rules -- An Analysis of Relevance Vector Machine Regression -- Cascade Classifiers for Hierarchical Decision Systems -- Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms -- Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification -- Soft Computing -- Partition Measures for Data Mining -- An Analysis of the FURIA Algorithm for Fuzzy Rule Induction -- Increasing Incompleteness of Data Sets—A Strategy for Inducing Better Rule Sets -- Knowledge Discovery Using Rough Set Theory -- Machine Learning Techniques for Prostate Ultrasound Image Diagnosis -- Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering -- Machine Learning for Robotics -- Automatic Selection of Object Recognition Methods Using Reinforcement Learning -- Comparison of Machine Learning for Autonomous Robot Discovery -- Multistrategy Learning for Robot Behaviours -- Neural Networks and Other Nature Inspired Approaches -- Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks -- Learning and Evolution of Autonomous Adaptive Agents -- Learning and Unlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis
    Additional Edition: Erscheint auch als Druckausgabe ISBN 978-3-642-05176-0
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Festschrift
    Author information: Raś, Zbigniew W. 1947-
    Author information: Kacprzyk, Janusz 1947-
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  • 4
    UID:
    almahu_9947364469502882
    Format: XVI, 269 p. 54 illus. , online resource.
    ISBN: 9783642386343
    Series Statement: Lecture Notes in Computer Science, 7912
    Content: This book constitutes the refereed proceedings of the International Conference on Intelligent Information Systems, IIS 2013, held in Warsaw, Poland in June 2013. The 28 full papers included in this volume were carefully reviewed and selected from 53 submissions. The contributions are organized in topical sections named: Natural language processing, text and Web mining, and machine learning and search.
    Note: Natural Language Processing -- A Hybrid Approach for Robust Multilingual Toponym Extraction and Disambiguation -- Towards a Polish LTAG Grammar -- Incorporating Head Recognition Into a CRF Chunker -- Classification of Predicate-Argument Relations in Polish Data -- Online Service for Polish Dependency Parsing and Results Visualisation -- The Scent of Deception: Recognizing Fake Perfume Reviews in Polish -- Question Classification for Polish Question Answering -- Chinese Named Entity Recognition with Conditional Random Fields in the Light of Chinese Characteristics -- Detecting Syntactic Errors in Dependency Treebanks for Morphosyntactically Rich Languages -- A Method for the Computational Representation of Croatian Morphology -- Mapping Named Entities from NKJP Corpus to Składnica Treebank and Polish Wordnet -- Automatic Detection of Annotation Errors in Polish-Language Corpora -- Unsupervised Induction of Persian Semantic Verb Classes Based on Syntactic Information -- Translation- and Projection-Based Unsupervised Coreference Resolution for Polish -- WCCL Match – A Language for Text Annotation -- Diachronic Corpus Based Word Semantic Variation and Change Mining -- A Representation of an Old Polish Dictionary Designed for Practical Applications -- Text and Web Mining -- Related Entity Finding Using Semantic Clustering Based on Wikipedia Categories -- Locality Sensitive Hashing for Similarity Search Using MapReduce on Large Scale Data -- Stabilization of Users Profiling Processed by Metaclustering of Web Pages -- Towards a Keyword-Focused Web Crawler -- Threshold ML-KNN: Statistical Evaluation on Multiple Benchmarks -- Supervised Content Visualization of Scientific Publications: A Case Study on the ArXiv Dataset -- A Calculus for Personalized Pagerank -- Machine Learning and Search -- Finding the Number of Clusters on the Basis of Eigenvectors -- Study on the Estimation of the Bipartite Graph Generator Parameters -- Expected Value of the Optimization Algorithm Outcome -- Solving Travelling Salesman Problem Using Egyptian Vulture Optimization Algorithm – A New Approach.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783642386336
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Konferenzschrift ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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  • 5
    UID:
    kobvindex_COL19585
    Format: 401, [1] s. ; 24 cm.
    Edition: [Wyd. 1]
    ISBN: 8320426847
    Language: Undetermined
    Subjects: General works
    RVK:
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  • 6
    UID:
    kobvindex_ZLB15161280
    Series Statement: Studies in computational intelligence ...
    Note: Text engl.
    Language: English
    Keywords: Maschinelles Lernen
    Author information: Michalski, Ryszard S.
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  • 7
    UID:
    b3kat_BV023524572
    Format: XXII, 431 S. , graph. Darst.
    Edition: 2. ed.
    ISBN: 1584882425
    Language: English
    Subjects: Economics
    RVK:
    Keywords: Statistische Prozesslenkung
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  • 8
    UID:
    almahu_9947364023702882
    Format: XXIV, 644 p. , online resource.
    ISBN: 9783540749769
    Series Statement: Lecture Notes in Computer Science, 4702
    Content: The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in machine learning and data mining. In 2007, the seventh collocated ECML/PKDD took place during September 17–21 on the centralcampus of WarsawUniversityand in the nearby Staszic Palace of the Polish Academy of Sciences. The conference for the third time used a hierarchical reviewing process. We nominated 30 Area Chairs, each of them responsible for one sub-?eld or several closely related research topics. Suitable areas were selected on the basis of the submission statistics for ECML/PKDD 2006 and for last year’s International Conference on Machine Learning (ICML 2006) to ensure a proper load balance amongtheAreaChairs.AjointProgramCommittee(PC)wasnominatedforthe two conferences, consisting of some 300 renowned researchers, mostly proposed by the Area Chairs. This joint PC, the largest of the series to date, allowed us to exploit synergies and deal competently with topic overlaps between ECML and PKDD. ECML/PKDD 2007 received 592 abstract submissions. As in previous years, toassistthereviewersandtheAreaChairsintheir?nalrecommendationauthors had the opportunity to communicate their feedback after the reviewing phase.
    Note: Invited Talks -- Learning, Information Extraction and the Web -- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation -- Mining Queries -- Adventures in Personalized Information Access -- Long Papers -- Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning -- Using the Web to Reduce Data Sparseness in Pattern-Based Information Extraction -- A Graphical Model for Content Based Image Suggestion and Feature Selection -- Efficient AUC Optimization for Classification -- Finding Transport Proteins in a General Protein Database -- Classification of Web Documents Using a Graph-Based Model and Structural Patterns -- Context-Specific Independence Mixture Modelling for Protein Families -- An Algorithm to Find Overlapping Community Structure in Networks -- Privacy Preserving Market Basket Data Analysis -- Feature Extraction from Sensor Data Streams for Real-Time Human Behaviour Recognition -- Generating Social Network Features for Link-Based Classification -- An Empirical Comparison of Exact Nearest Neighbour Algorithms -- Site-Independent Template-Block Detection -- Statistical Model for Rough Set Approach to Multicriteria Classification -- Classification of Anti-learnable Biological and Synthetic Data -- Improved Algorithms for Univariate Discretization of Continuous Features -- Efficient Weight Learning for Markov Logic Networks -- Classification in Very High Dimensional Problems with Handfuls of Examples -- Domain Adaptation of Conditional Probability Models Via Feature Subsetting -- Learning to Detect Adverse Traffic Events from Noisily Labeled Data -- IKNN: Informative K-Nearest Neighbor Pattern Classification -- Finding Outlying Items in Sets of Partial Rankings -- Speeding Up Feature Subset Selection Through Mutual Information Relevance Filtering -- A Comparison of Two Approaches to Classify with Guaranteed Performance -- Towards Data Mining Without Information on Knowledge Structure -- Relaxation Labeling for Selecting and Exploiting Efficiently Non-local Dependencies in Sequence Labeling -- Bridged Refinement for Transfer Learning -- A Prediction-Based Visual Approach for Cluster Exploration and Cluster Validation by HOV3 -- Short Papers -- Flexible Grid-Based Clustering -- Polyp Detection in Endoscopic Video Using SVMs -- A Density-Biased Sampling Technique to Improve Cluster Representativeness -- Expectation Propagation for Rating Players in Sports Competitions -- Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract) -- Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach -- Realistic Synthetic Data for Testing Association Rule Mining Algorithms for Market Basket Databases -- Learning Multi-dimensional Functions: Gas Turbine Engine Modeling -- Constructing High Dimensional Feature Space for Time Series Classification -- A Dynamic Clustering Algorithm for Mobile Objects -- A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions -- MINI: Mining Informative Non-redundant Itemsets -- Stream-Based Electricity Load Forecast -- Automatic Hidden Web Database Classification -- Pruning Relations for Substructure Discovery of Multi-relational Databases -- The Most Reliable Subgraph Problem -- Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains -- Searching for Better Randomized Response Schemes for Privacy-Preserving Data Mining -- Pre-processing Large Spatial Data Sets with Bayesian Methods -- Tag Recommendations in Folksonomies -- Providing Naïve Bayesian Classifier-Based Private Recommendations on Partitioned Data -- Multi-party, Privacy-Preserving Distributed Data Mining Using a Game Theoretic Framework -- Multilevel Conditional Fuzzy C-Means Clustering of XML Documents -- Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder -- Real Time GPU-Based Fuzzy ART Skin Recognition -- A Cooperative Game Theoretic Approach to Prototype Selection -- Dynamic Bayesian Networks for Real-Time Classification of Seismic Signals -- Robust Visual Mining of Data with Error Information -- An Effective Approach to Enhance Centroid Classifier for Text Categorization -- Automatic Categorization of Human-Coded and Evolved CoreWar Warriors -- Utility-Based Regression -- Multi-label Lazy Associative Classification -- Visual Exploration of Genomic Data -- Association Mining in Large Databases: A Re-examination of Its Measures -- Semantic Text Classification of Emergent Disease Reports.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540749752
    Language: English
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  • 9
    UID:
    almahu_BV036048920
    Format: XIX, 529 S. : , Ill., graph. Darst.
    ISBN: 978-3-642-05178-4
    Series Statement: Studies in computational intelligence 263
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Festschrift
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  • 10
    UID:
    almahu_9947364024202882
    Format: XXIV, 812 p. , online resource.
    ISBN: 9783540749585
    Series Statement: Lecture Notes in Computer Science, 4701
    Content: The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in machine learning and data mining. In 2007, the seventh collocated ECML/PKDD took place during September 17–21 on the centralcampus of WarsawUniversityand in the nearby Staszic Palace of the Polish Academy of Sciences. The conference for the third time used a hierarchical reviewing process. We nominated 30 Area Chairs, each of them responsible for one sub-?eld or several closely related research topics. Suitable areas were selected on the basis of the submission statistics for ECML/PKDD 2006 and for last year’s International Conference on Machine Learning (ICML 2006) to ensure a proper load balance amongtheAreaChairs.AjointProgramCommittee(PC)wasnominatedforthe two conferences, consisting of some 300 renowned researchers, mostly proposed by the Area Chairs. This joint PC, the largest of the series to date, allowed us to exploit synergies and deal competently with topic overlaps between ECML and PKDD. ECML/PKDD 2007 received 592 abstract submissions. As in previous years, toassistthereviewersandtheAreaChairsintheir?nalrecommendationauthors had the opportunity to communicate their feedback after the reviewing phase.
    Note: Invited Talks -- Learning, Information Extraction and the Web -- Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation -- Mining Queries -- Adventures in Personalized Information Access -- Long Papers -- Statistical Debugging Using Latent Topic Models -- Learning Balls of Strings with Correction Queries -- Neighborhood-Based Local Sensitivity -- Approximating Gaussian Processes with -Matrices -- Learning Metrics Between Tree Structured Data: Application to Image Recognition -- Shrinkage Estimator for Bayesian Network Parameters -- Level Learning Set: A Novel Classifier Based on Active Contour Models -- Learning Partially Observable Markov Models from First Passage Times -- Context Sensitive Paraphrasing with a Global Unsupervised Classifier -- Dual Strategy Active Learning -- Decision Tree Instability and Active Learning -- Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering -- The Cost of Learning Directed Cuts -- Spectral Clustering and Embedding with Hidden Markov Models -- Probabilistic Explanation Based Learning -- Graph-Based Domain Mapping for Transfer Learning in General Games -- Learning to Classify Documents with Only a Small Positive Training Set -- Structure Learning of Probabilistic Relational Models from Incomplete Relational Data -- Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA -- Bayesian Substructure Learning - Approximate Learning of Very Large Network Structures -- Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs -- Source Separation with Gaussian Process Models -- Discriminative Sequence Labeling by Z-Score Optimization -- Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches -- Bayesian Inference for Sparse Generalized Linear Models -- Classifier Loss Under Metric Uncertainty -- Additive Groves of Regression Trees -- Efficient Computation of Recursive Principal Component Analysis for Structured Input -- Hinge Rank Loss and the Area Under the ROC Curve -- Clustering Trees with Instance Level Constraints -- On Pairwise Naive Bayes Classifiers -- Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models -- Safe Q-Learning on Complete History Spaces -- Random k-Labelsets: An Ensemble Method for Multilabel Classification -- Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble -- Avoiding Boosting Overfitting by Removing Confusing Samples -- Planning and Learning in Environments with Delayed Feedback -- Analyzing Co-training Style Algorithms -- Policy Gradient Critics -- An Improved Model Selection Heuristic for AUC -- Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators -- Short Papers -- Stepwise Induction of Multi-target Model Trees -- Comparing Rule Measures for Predictive Association Rules -- User Oriented Hierarchical Information Organization and Retrieval -- Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character Recognition -- Weighted Kernel Regression for Predicting Changing Dependencies -- Counter-Example Generation-Based One-Class Classification -- Test-Cost Sensitive Classification Based on Conditioned Loss Functions -- Probabilistic Models for Action-Based Chinese Dependency Parsing -- Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search -- A Simple Lexicographic Ranker and Probability Estimator -- On Minimizing the Position Error in Label Ranking -- On Phase Transitions in Learning Sparse Networks -- Semi-supervised Collaborative Text Classification -- Learning from Relevant Tasks Only -- An Unsupervised Learning Algorithm for Rank Aggregation -- Ensembles of Multi-Objective Decision Trees -- Kernel-Based Grouping of Histogram Data -- Active Class Selection -- Sequence Labeling with Reinforcement Learning and Ranking Algorithms -- Efficient Pairwise Classification -- Scale-Space Based Weak Regressors for Boosting -- K-Means with Large and Noisy Constraint Sets -- Towards ‘Interactive’ Active Learning in Multi-view Feature Sets for Information Extraction -- Principal Component Analysis for Large Scale Problems with Lots of Missing Values -- Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling -- Class Noise Mitigation Through Instance Weighting -- Optimizing Feature Sets for Structured Data -- Roulette Sampling for Cost-Sensitive Learning -- Modeling Highway Traffic Volumes -- Undercomplete Blind Subspace Deconvolution Via Linear Prediction -- Learning an Outlier-Robust Kalman Filter -- Imitation Learning Using Graphical Models -- Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks -- Semi-definite Manifold Alignment -- General Solution for Supervised Graph Embedding -- Multi-objective Genetic Programming for Multiple Instance Learning -- Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540749578
    Language: English
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