08879oam 2200601 450 99646558010331620210520111512.03-540-74958-610.1007/978-3-540-74958-5(CKB)1000000000490772(SSID)ssj0000318743(PQKBManifestationID)11222364(PQKBTitleCode)TC0000318743(PQKBWorkID)10311748(PQKB)10157564(DE-He213)978-3-540-74958-5(MiAaPQ)EBC3063474(MiAaPQ)EBC6413197(MiAaPQ)EBC6413202(PPN)123165113(EXLCZ)99100000000049077220210520d2007 uy 0engurnn#008mamaatxtccrMachine learning ECML 2007 : 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 : proceedings /edited by Joost N. Kok [and four others]1st ed. 2007.Berlin, Germany ;New York, New York :Springer,[2007]1 online resource (XXIV, 812 p.)Lecture notes in computer science. Lecture notes in artificial intelligence ;4701Bibliographic Level Mode of Issuance: Monograph3-540-74957-8 Includes bibliographical references and index.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.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.Lecture notes in computer science.Lecture notes in artificial intelligence ;4701.European Conference on Machine LearningECML 200718th European Conference on Machine LearningEighteenth European Conference on Machine LearningMachine learningCongressesMachine learning006.31Kok Joost N.MiAaPQMiAaPQUtOrBLWBOOK996465580103316Machine learning257234UNISA04901nam 2200889 450 991080974400332120220207214514.00-271-08820-60-271-08822-210.1515/9780271088228(CKB)4100000011216033(MiAaPQ)EBC6224566(DE-B1597)583707(DE-B1597)9780271088228(OCoLC)1253314086(EXLCZ)99410000001121603320200930d2020 ub 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierContraband guides race, transatlantic culture, and the arts in the Civil War era /Paul H. D. KaplanUniversity Park, Pennsylvania :The Pennsylvania State University Press,[2020]©20201 online resource (313 pages)0-271-08385-9 Includes bibliographical references and index.Front matter --Contents --List of illustrations --Acknowledgments --Introduction --1 Representations of People of Color in Nineteenth-Century American Accounts of Italian Travel --2 “A Mulatto Sculptor from New Orleans” --3 “The Black Man To-day Means Liberty” --4 “Something American” --5 Old Masters --6 Contraband Guide --Notes --Bibliography --IndexIn his best-selling travel memoir, The Innocents Abroad, Mark Twain punningly refers to the black man who introduces him to Venetian Renaissance painting as a “contraband guide,” a term coined to describe fugitive slaves who assisted Union armies during the Civil War. By means of this and similar case studies, Paul H. D. Kaplan documents the ways in which American cultural encounters with Europe and its venerable artistic traditions influenced nineteenth-century concepts of race in the United States.Americans of the Civil War era were struck by the presence of people of color in European art and society, and American artists and authors, both black and white, adapted and transformed European visual material to respond to the particular struggles over the identity of African Americans. Taking up the work of both well- and lesser-known artists and writers—such as the travel writings of Mark Twain and William Dean Howells, the paintings of German American Emanuel Leutze, the epistolary exchange between John Ruskin and Charles Eliot Norton, newspaper essays written by Frederick Douglass and William J. Wilson, and the sculpture of freed slave Eugène Warburg—Kaplan lays bare how racial attitudes expressed in mid-nineteenth-century American art were deeply inflected by European traditions. By highlighting the contributions people of black African descent made to the fine arts in the United States during this period, along with the ways in which they were represented, Contraband Guides provides a fresh perspective on the theme of race in Civil War–era American art. It will appeal to art historians, to specialists in African American studies and American studies, and to general readers interested in American art and African American history.African American artEuropean influencesAfrican American art19th centuryArt, American19th centuryAfrican Americans in artHistory19th centuryArt and raceHistory19th centuryBlack people in artHistory19th centuryAbraham Lincoln.Adoration of the Magi.African American.Afro-European.Billy Lee.Charles Eliot Norton.Civil War.Emanuel Leutze.Eugène Warburg.Frederick Douglass.George Washington.Harriet Beecher Stowe.Jacopo Tintoretto.John Hay.John Ruskin.Joshua Bowen Smith.Mark Twain.Neoclassical sculpture.Paolo Veronese.Pierre Soulé.Race.Slavery.Transatlantic.William Cooper Nell.William Dean Howells.William J. Wilson.African American artEuropean influences.African American artArt, AmericanAfrican Americans in artHistoryArt and raceHistoryBlack people in artHistory704.0396073Kaplan Paul H. D(Paul Henry Daniel),1952-1711139MiAaPQMiAaPQMiAaPQBOOK9910809744003321Contraband guides4102245UNINA