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Challenges in Computational Statistics and Data Mining / / edited by Stan Matwin, Jan Mielniczuk
| Challenges in Computational Statistics and Data Mining / / edited by Stan Matwin, Jan Mielniczuk |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (X, 399 p. 73 illus., 3 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Data mining Statistics Artificial intelligence Computational Intelligence Data Mining and Knowledge Discovery Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Artificial Intelligence |
| ISBN | 3-319-18781-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Evolutionary Computation for Real-world Problems -- Selection of Significant Features Using Monte Carlo Feature Selection -- ADX Algorithm for Supervised Classification -- Estimation of Entropy from Subword Complexity -- Exact Rates of Convergence of Kernel-based Classification Rule -- Compound Bipolar Queries: a Step Towards an Enhanced Human Consistency and Human Friendliness -- Process Inspection by Attributes Using Predicted Data -- Székely Regularization for Uplift Modeling -- Dominance-Based Rough Set Approach to Multiple Criterion Ranking with Sorting-specific Preference Information -- On things not Seen -- Network Capacity Bound for Personalized Bipartite Page Rank -- Dependence Factor as a Rule Evaluation Measure -- Recent Results on Quantlie Estimation Methods in Simulation Model -- Adaptive Monte Carlo Maximum Likelihood -- What Do we Choose when we Err? Model Selection and Testing for Misspecified Logistic Regression Revisited -- Semiparametric Inference Identification of Block-oriented Systems -- Dealing with Data Difficulty Factors While Learning from Imbalanced Data -- Privacy Protection in a Time of Big Data -- Data Based Modeling. |
| Record Nr. | UNINA-9910254212903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
| Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods |
| Autore | Mielniczuk Jan |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (226 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Mechanical engineering & materials |
| Soggetto non controllato |
high-dimensional time series
nonstationarity network estimation change points kernel estimation high-dimensional regression loss function random predictors misspecification consistent selection subgaussianity generalized information criterion robustness statistical learning theory information theory entropy parameter estimation learning systems privacy prediction methods misclassification risk model misspecification penalized estimation supervised classification variable selection consistency archimedean copula consistency estimation extreme-value copula tail dependency multivariate analysis conditional mutual information CMI information measures nonparametric variable selection criteria gaussian mixture conditional infomax feature extraction CIFE joint mutual information criterion JMI generative tree model Markov blanket minimum distance estimation maximum likelihood estimation influence functions adaptive splines B-splines right-censored data semiparametric regression synthetic data transformation time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576873203321 |
Mielniczuk Jan
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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