Â1: ÂPrimum volumen. Aristotelis Stagiritae Organum. Auerrois Cordubensis in eo commentaria, epitome, quaesita nonnulla, ac epistola vna. ..

• Record Nr.: UNISANNIOBVEE016276
• Autore: Aristoteles
• Pubbl/distr/stampa: Venetiis : apud Iunctas, 1562 ( (Venetijs) : apud haeredes Luceantonij Iuntae, 1562
• Descrizione fisica: 3 v. ; 8°
• Lingua di pubblicazione: Latino
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge

• Record Nr.: UNISA996200359403316
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
• ISBN: 3-319-23528-1
• Edizione: [1st ed. 2015.]
• Descrizione fisica: 1 online resource (LVIII, 709 p. 160 illus.)
• Collana: Lecture Notes in Artificial Intelligence ; ; 9284
• Disciplina: 006.31
• Soggetti: Data mining; Artificial intelligence; Pattern recognition; Information storage and retrieval; Database management; Application software; Data Mining and Knowledge Discovery; Artificial Intelligence; Pattern Recognition; Information Storage and Retrieval; Database Management; Information Systems Applications (incl. Internet)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di contenuto: Intro -- Preface -- Organization -- Abstracts of Invited Talks -- Towards Declarative, Domain-OrientedData Analysis -- Sum-Product Networks: Deep Modelswith Tractable Inference -- Mining Online Networks and Communities -- Learning to Acquire Knowledge in a SmartGrid Environment -- Untangling the Web's Invisible Net -- Towards a Digital Time Machine Fueled by BigData and Social Mining -- Abstracts of Journal Track Articles -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Research Track Classification, Regression and Supervised Learning -- Data Split Strategiesfor Evolving Predictive Models -- 1 Introduction -- 2 Data Splits for Model Fitting, Selection,and Assessment -- 3 Issues with Evolving Models -- 4 Data Splits for Evolving Models -- 4.1 Parallel Dump Workflow -- 4.2 Serial Waterfall Workflow -- 4.3 Hybrid Workflow -- 5 Bias Due to Test Set Reuse -- 6 Illustration on Synthetic Data -- 7 Case Study: Paraphrase Detection -- 8 Related Work -- 9 Conclusions -- A Appendix: Bias Due to Test Set Reuse -- References -- Discriminative Interpolation for Classification of Functional Data -- 1 Introduction -- 2 Function Representations and Wavelets -- 3 Related Work -- 4 Classification by Discriminative Interpolation -- 4.1 Training Formulation -- 4.2 Testing Formulation -- 5 Experiments -- 6 Conclusion -- References -- Fast Label Embeddings via Randomized Linear Algebra -- 1 Introduction -- 1.1 Contributions -- 2 Algorithm Derivation -- 2.1 Notation -- 2.2 Background -- 2.3 Rank-Constrained Estimation and Embedding -- 2.4 Rembrandt -- 3 Related Work -- 4 Experiments -- 4.1 ALOI -- 4.2 ODP -- 4.3 LSHTC -- 5 Discussion -- References -- Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation -- 1 Introduction -- 2 General Idea -- 3 Theory -- 4 Closed form Solution for Objective and its Gradient.; 5 Experiments -- 6 Conclusions -- References -- Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration -- 1 Introduction -- 2 Proper Scoring Rules -- 2.1 Scoring Rules -- 2.2 Divergence, Entropy and Properness -- 2.3 Expected Loss and Empirical Loss -- 3 Decompositions with Ideal Scores and Calibrated Scores -- 3.1 Ideal Scores Q and the Decomposition L=EL+IL -- 3.2 Calibrated Scores C and the Decomposition L=CL+RL -- 4 Adjusted Scores A and the Decomposition L=AL+PL -- 4.1 Adjustment -- 4.2 The Right Adjustment Procedure Guarantees Decreased Loss -- 5 Decomposition Theorems and Terminology -- 5.1 Decompositions with S,C,Q,Y -- 5.2 Decompositions with S,A,C,Q,Y and Terminology -- 6 Algorithms and Experiments -- 7 Related Work -- 8 Conclusions -- References -- Parameter Learning of Bayesian Network Classifiers Under Computational Constraints -- 1 Introduction -- 2 Related Work -- 3 Background and Notation -- 4 Algorithms for Online Learning of Reduced-Precision Parameters -- 4.1 Learning Maximum Likelihood Parameters -- 4.2 Learning Maximum Margin Parameters -- 5 Experiments -- 5.1 Datasets -- 5.2 Results -- 6 Discussions -- References -- Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning -- 1 Introduction -- 2 Multi-label Classification -- 3 Model Description -- 3.1 Joint Space Embeddings -- 3.2 Learning with Hierarchical Structures Over Labels -- 3.3 Efficient ; Gradients Computation -- 3.4 Label Ranking to Binary Predictions -- 4 Experimental Setup -- 5 Experimental Results -- 5.1 Learning All Labels Together -- 5.2 Learning to Predict Unseen Labels -- 6 Pretrained Label Embeddings as Good Initial Guess -- 6.1 Understanding Label Embeddings -- 6.2 Results -- 7 Conclusions -- Regression with Linear Factored Functions -- 1 Introduction -- 1.1 Kernel Regression.; 1.2 Factored Basis Functions -- 2 Regression -- 3 Linear Factored Functions -- 3.1 Function Class -- 3.2 Constraints -- 3.3 Regularization -- 3.4 Optimization -- 4 Empirical Evaluation -- 4.1 Demonstration -- 4.2 Evaluation -- 5 Discussion -- Appendix A LFF Definition and Properties -- Appendix B Inner Loop Derivation -- Appendix C Proofs of the Propositions -- References -- Ridge Regression, Hubness, and Zero-Shot Learning -- 1 Introduction -- 1.1 Background -- 1.2 Research Objective and Contributions -- 2 Zero-Shot Learning as a Regression Problem -- 3 Hubness Phenomenon and the Variance of Data -- 4 Hubness in Regression-Based Zero-Shot Learning -- 4.1 Shrinkage of Projected Objects -- 4.2 Influence of Shrinkage on Nearest Neighbor Search -- 4.3 Additional Argument for Placing Target Objects Closer to the Origin -- 4.4 Summary of the Proposed Approach -- 5 Related Work -- 6 Experiments -- 6.1 Experimental Setups -- 6.2 Task Descriptions and Datasets -- 6.3 Experimental Results -- 7 Conclusion -- References -- Solving Prediction Games with Parallel Batch Gradient Descent -- 1 Introduction -- 2 Problem Setting and Data Transformation Model -- 3 Analysis of Equilibrium Points -- 3.1 Existence of Equilibrium Points -- 3.2 Uniqueness of Equilibrium Points -- 4 Finding the Unique Equilibrium Point Efficiently -- 4.1 Inexact Line Search -- 4.2 Arrow-Hurwicz-Uzawa Method -- 4.3 Parallelized Methods -- 5 Experimental Results -- 5.1 Reference Methods -- 5.2 Performance of the Parameterized Transformation Model -- 5.3 Optimization Algorithms -- 5.4 Parallelized Models -- 6 Conclusion -- References -- Structured Regularizer for Neural Higher-Order Sequence Models -- 1 Introduction -- 2 Related Work -- 3 Higher-Order Conditional Random Fields -- 3.1 Parameter Learning -- 3.2 Forward Algorithm for 2nd-Order CRFs -- 4 Structured Regularizer -- 5 Experiments.; 5.1 TIMIT Data Set -- 5.2 Experimental Setup -- 5.3 Labeling Results Using Only MLP Networks -- 5.4 Labeling Results Using LC-CRFs with Linear or Neural Higher-Order Factors -- 6 Conclusion -- References -- Versatile Decision Trees for Learning Over Multiple Contexts -- 1 Introduction -- 2 Dataset Shift -- 3 Versatile Decision Trees -- 3.1 Constructing Splits Using Percentiles -- 3.2 Adapting for Output Shifts -- 3.3 Versatile Model for Decision Trees -- 4 Experimental Results -- 4.1 Generating Synthetic Shifts -- 4.2 Results of the Synthetic Shifts -- 4.3 Results on Non-synthetic Shifts -- 5 Conclusion -- References -- When is Undersampling Effective in Unbalanced Classification Tasks? -- 1 Introduction -- 2 The Warping Effect of Undersampling on the Posterior Probability -- 3 The Interaction Between Warping and Variance of the Estimator -- 4 Experimental Validation -- 4.1 Synthetic Datasets -- 4.2 Real Datasets -- 5 Conclusion -- References -- Clustering and Unsupervised Learning -- A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisons -- 1 Introduction -- 2 Related Work -- 3 Kernel Learning with Relative Distances -- 3.1 Basic Definitions -- 3.2 Relative Distance Constraints -- 3.3 Extension to a Kernel Space -- 3.4 Log Determinant Divergence for Kernel Learning -- 3.5 Problem Definition -- 4 Semi-supervised Kernel Learning -- 4.1 Bregman Projections for Constrained Optimization -- 4.2 Semi-supervised Kernel Learning with Relative ; Comparisons -- Selecting the Bandwidth Parameter. -- Semi-Supervised Kernel Learning with Relative Comparisons. -- Clustering Method. -- 5 Experimental Results -- 5.1 Datasets -- 5.2 Relative Constraints vs. Pairwise Constraints -- 5.3 Multi-resolution Analysis -- 5.4 Generalization Performance -- 5.5 Effect of Equality Constraints -- 6 Conclusion -- References.; Bayesian Active Clustering with Pairwise Constraints -- 1 Introduction -- 2 Problem Statement -- 3 Bayesian Active Clustering -- 3.1 The Bayesian Clustering Model -- Marginalization of Cluster Labels. -- 3.2 Active Query Selection -- Selection Criteria. -- Computing the Selection Objectives. -- 3.3 The Sequential MCMC Sampling of W -- 3.4 Find the MAP Solution -- 4 Experiments -- 4.1 Dataset and Setup -- 4.2 Effectiveness of the Proposed Clustering Model -- 4.3 Effectiveness of the Overall Active Clustering Model -- 4.4 Analysis of the Acyclic Graph Restriction -- 5 Related Work -- 6 Conclusion -- References -- ConDist: A Context-Driven Categorical Distance Measure -- 1 Introduction -- 2 Related Work -- 3 The Distance Measure ConDist -- 3.1 Definition of ConDist -- 3.2 Attribute Distance dX -- 3.3 Attribute Weighting Function wX -- 3.4 Correlation, Context and Impact -- 3.5 Heterogeneous Data Sets -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Experiment 1 -- Context Attribute Selection -- 4.3 Experiment 2 -- Comparison in the Context of Classification -- 4.4 Experiment 3 -- Comparison in the Context of Clustering -- 5 Discussion -- 5.1 Experiment 1 -- Context Attribute Selection -- 5.2 Experiment 2 -- Comparison in the Context of Classification -- 5.3 Experiment 3 -- Comparison in the Context of Clustering -- 6 Summary -- References -- Discovering Opinion Spammer Groups by Network Footprints -- 1 Introduction -- 2 Measuring Network Footprints -- 2.1 Neighbor Diversity of Nodes -- 2.2 Self-Similarity in Real-World Graphs -- 2.3 NFS Measure -- 3 Detecting Spammer Groups -- 4 Evaluation -- 4.1 Performance of NFS on Synthetic Data -- 4.2 Performance of GroupStrainer on Synthetic Data -- 4.3 Results on Real-World Data -- 5 Related Work -- 6 Conclusion -- References -- Gamma Process Poisson Factorization for Joint Modeling of Network and Documents.; 1 Introduction.
• Sommario/riassunto: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

"C37.012-2022 - IEEE Guide for the Application of Capacitive Current Switching for AC High-Voltage Circuit Breakers Above 1000 V" / / IEEE

• Record Nr.: UNISA996575615503316
• Pubbl/distr/stampa: New York : , : IEEE, , 2023; ©2023
• ISBN: 1504493133; 9781504493130
• Descrizione fisica: 1 online resource (90 pages)
• Disciplina: 621.317
• Soggetti: Electric circuit-breakers
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Research Papers in Statistical Inference for Time Series and Related Models : Essays in Honor of Masanobu Taniguchi / / edited by Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa

• Record Nr.: UNINA9910728935303321
• Autore: Liu Yan
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
• ISBN: 9789819908035; 9819908035
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (591 pages)
• Altri autori (Persone): HirukawaJunichi; KakizawaYoshihide
• Disciplina: 519.55
• Soggetti: Time-series analysis; Mathematical statistics; Nonparametric statistics; Time Series Analysis; Parametric Inference; Non-parametric Inference; Mathematical Statistics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1. Frequency domain empirical likelihood method for infinite variance models -- Chapter 2. Diagnostic testing for time series -- Chapter 3. Statistical Inference for Glaucoma Detection -- Chapter 4. On Hysteretic Vector Autoregressive Model with Applications -- Chapter 5. Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression -- Chapter 6. Exact topological inference on resting-state brain networks -- Chapter 7. An Introduction to Geostatistics -- Chapter 8. Relevant change points in high dimensional time series -- Chapter 9. Adaptiveness of the empirical distribution of residuals in semi-parametric conditional location scale models -- Chapter 10. Standard testing procedures for white noise and heteroskedasticity -- Chapter 11. Estimation of Trigonometric Moments for Circular Binary Series -- Chapter 12. Time series analysis with unsupervised learning -- Chapter 13. Recovering the market volatility shocks in high-dimensional time series -- Chapter14. Asymptotic properties of mildly explosive processes with locally stationary disturbance -- Chapter 15. Multi-Asset Empirical Martingale Price Estimators for Financial Derivatives -- Chapter 16. Consistent Order Selection for ARFIMA Processes -- Chapter 17. Recursive asymmetric kernel density estimation for nonnegative data -- Chapter 18. Fitting an error distribution in some heteroscedastic time series models -- Chapter 19. Symbolic Interval-Valued Data Analysis for Time Series Based on Auto-Interval-Regressive Models -- Chapter 20. ROBUST LINEAR INTERPOLATION AND EXTRAPOLATION OF STATIONARY TIME SERIES -- Chapter 21. Non Gaussian models for fMRI data -- Chapter 22. Robust inference for ordinal response models -- Chapter 23. Change point problems for diffusion processes and time series models -- Chapter 24. Empirical likelihood approach for time series -- Chapter 25. Exploring the Dependence Structure Between Oscillatory Activities in Multivariate Time Series -- Chapter 26. Projection-based nonparametric goodness-of-fit testing with functional data.
• Sommario/riassunto: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the readerwith comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a ; simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.