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ORCIDPawel Teisseyre
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2020 – today
- 2024
[j18]Wangduk Seo
, Sung-Hyun Cho, Pawel Teisseyre, Jaesung Lee:
A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features. IEEE Access 12: 245-257 (2024)- [j17]Wojciech Rejchel, Pawel Teisseyre, Jan Mielniczuk:
Joint empirical risk minimization for instance-dependent positive-unlabeled data. Knowl. Based Syst. 304: 112444 (2024) - [c11]Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk:
Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. ECAI 2024: 1672-1679 - [i6]Pawel Teisseyre, Konrad Furmanczyk, Jan Mielniczuk:
Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning. CoRR abs/2404.00145 (2024) - [i5]Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee:
Cost-constrained multi-label group feature selection using shadow features. CoRR abs/2408.01851 (2024) - 2023
- [j16]Tae-Won Lee, Pawel Teisseyre, Jaesung Lee:
Effective Exploitation of Macroeconomic Indicators for Stock Direction Classification Using the Multimodal Fusion Transformer. IEEE Access 11: 10275-10287 (2023) - [j15]Tomasz Klonecki, Pawel Teisseyre:
Feature selection under budget constraint in medical applications: analysis of penalized empirical risk minimization methods. Appl. Intell. 53(24): 29943-29973 (2023) - [j14]Pawel Teisseyre, Jaesung Lee:
Multilabel all-relevant feature selection using lower bounds of conditional mutual information. Expert Syst. Appl. 216: 119436 (2023) - [j13]Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee:
Cost-constrained feature selection in multilabel classification using an information-theoretic approach. Pattern Recognit. 141: 109605 (2023) - [c10]Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre:
Double Logistic Regression Approach to Biased Positive-Unlabeled Data. ECAI 2023: 764-771 - [c9]Tomasz Klonecki, Pawel Teisseyre, Jaesung Lee:
Cost-constrained Group Feature Selection Using Information Theory. MDAI 2023: 121-132 - [i4]Wojciech Rejchel, Pawel Teisseyre, Jan Mielniczuk:
Joint empirical risk minimization for instance-dependent positive-unlabeled data. CoRR abs/2312.16557 (2023) - 2022
- [j12]Pawel Teisseyre:
Joint Feature Selection and Classification for Positive Unlabelled Multi-Label Data Using Weighted Penalized Empirical Risk Minimization. Int. J. Appl. Math. Comput. Sci. 32(2): 311-322 (2022) - [i3]Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Pawel Teisseyre:
Joint estimation of posterior probability and propensity score function for positive and unlabelled data. CoRR abs/2209.07787 (2022) - 2021
- [j11]Malgorzata Lazecka, Jan Mielniczuk, Pawel Teisseyre:
Estimating the class prior for positive and unlabelled data via logistic regression. Adv. Data Anal. Classif. 15(4): 1039-1068 (2021) - [j10]Mariusz Kubkowski, Jan Mielniczuk, Pawel Teisseyre:
How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information. J. Mach. Learn. Res. 22: 62:1-62:57 (2021) - [j9]Pawel Teisseyre:
Classifier chains for positive unlabelled multi-label learning. Knowl. Based Syst. 213: 106709 (2021) - [c8]Pawel Teisseyre, Tomasz Klonecki:
Controlling Costs in Feature Selection: Information Theoretic Approach. ICCS (2) 2021: 483-496 - [c7]Jan Mielniczuk, Pawel Teisseyre:
Detection of Conditional Dependence Between Multiple Variables Using Multiinformation. ICCS (6) 2021: 677-690 - 2020
- [c6]Pawel Teisseyre:
Learning Classifier Chains Using Matrix Regularization: Application to Multimorbidity Prediction. ECAI 2020: 1531-1538 - [c5]Pawel Teisseyre, Jan Mielniczuk, Malgorzata Lazecka:
Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data. ICCS (4) 2020: 3-17 - [c4]Pawel Teisseyre, Jan Mielniczuk, Michal J. Dabrowski:
Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique. ICCS (3) 2020: 511-524
2010 – 2019
- 2019
- [j8]Jan Mielniczuk, Pawel Teisseyre:
Stopping rules for mutual information-based feature selection. Neurocomputing 358: 255-274 (2019) - [j7]Pawel Teisseyre, Damien Zufferey, Marta Slomka:
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification. Pattern Recognit. 86: 290-319 (2019) - 2018
- [c3]Mateusz Pawluk, Pawel Teisseyre, Jan Mielniczuk:
Information-Theoretic Feature Selection Using High-Order Interactions. LOD 2018: 51-63 - 2017
- [j6]Pawel Teisseyre:
CCnet: Joint multi-label classification and feature selection using classifier chains and elastic net regularization. Neurocomputing 235: 98-111 (2017) - [j5]Marcin Sydow, Katarzyna Baraniak, Pawel Teisseyre:
Diversity of editors and teams versus quality of cooperative work: experiments on wikipedia. J. Intell. Inf. Syst. 48(3): 601-632 (2017) - 2016
- [j4]Pawel Teisseyre, Robert A. Klopotek, Jan Mielniczuk:
Random Subspace Method for high-dimensional regression with the R package regRSM. Comput. Stat. 31(3): 943-972 (2016) - [j3]Pawel Teisseyre:
Feature ranking for multi-label classification using Markov networks. Neurocomputing 205: 439-454 (2016) - [p1]Jan Mielniczuk, Pawel Teisseyre:
What Do We Choose When We Err? Model Selection and Testing for Misspecified Logistic Regression Revisited. Challenges in Computational Statistics and Data Mining 2016: 271-296 - [i2]Pawel Teisseyre:
Feature ranking for multi-label classification using Markov Networks. CoRR abs/1602.07464 (2016) - [i1]Pawel Teisseyre:
Asymptotic consistency and order specification for logistic classifier chains in multi-label learning. CoRR abs/1602.07466 (2016) - 2015
- [c2]Piotr Przybyla, Pawel Teisseyre:
What Do Your Look-alikes Say about You? Exploiting Strong and Weak Similarities for Author Profiling. CLEF (Working Notes) 2015 - 2014
- [j2]Jan Mielniczuk, Pawel Teisseyre:
Using random subspace method for prediction and variable importance assessment in linear regression. Comput. Stat. Data Anal. 71: 725-742 (2014) - [j1]Piotr Przybyla, Pawel Teisseyre:
Analysing Utterances in Polish Parliament to Predict Speaker's Background. J. Quant. Linguistics 21(4): 350-376 (2014) - 2011
- [c1]Jan Mielniczuk, Pawel Teisseyre:
Model Selection in Logistic Regression Using p-Values and Greedy Search. SIIS 2011: 128-141
Coauthor Index
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last updated on 2024-10-28 21:15 CET by the dblp team
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