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ORCIDKrisztián Búza
Person information
- affiliation: Hungarian Academy of Sciences, Budapest, Hungary
- affiliation: Semmelweis University, Budapest, Hungary
- affiliation: University of Hildesheim, Germany
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2020 – today
- 2023
[c40]Krisztián Búza
:
Sparsity-Invariant Convolution for Forecasting Irregularly Sampled Time Series. ICCCI 2023: 151-162- [c39]Krisztián Búza, Kamil Ksiazek, Wilhelm Masarczyk, Przemyslaw Glomb, Piotr Gorczyca, Magdalena Piegza:
A Simple and Effective Classifier for the Detection of Schizophrenia and Bipolar Disorder based on Heart Rate Variability Time Series. ITAT 2023: 217-222 - 2022
- [c38]Bartosz Grabowski, Przemyslaw Glomb, Kamil Ksiazek, Krisztián Búza:
Improving Autoencoders Performance for Hyperspectral Unmixing Using Clustering. ACIIDS (Companion) 2022: 102-121 - [c37]Kamil Ksiazek, Przemyslaw Glomb, Michal Romaszewski, Michal Cholewa, Bartosz Grabowski, Krisztián Búza:
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation. ICIAP (1) 2022: 391-403 - [c36]Ladislav Peska, Patrik Veselý, Tomás Skopal, Krisztián Búza:
Person Authentication using Visual Representations of Keyboard Typing Dynamics. SNAMS 2022: 1-6 - 2021
- [j12]Margit Antal, Krisztián Búza, Norbert Fejér:
SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories. IEEE Access 9: 124396-124408 (2021) - [c35]Krisztián Búza, Margit Antal:
Convolutional Neural Networks with Dynamic Convolution for Time Series Classification. ICCCI (CCIS Volume) 2021: 304-312 - [c34]Margit Antal, Norbert Fejér, Krisztián Búza:
SapiMouse: Mouse Dynamics-based User Authentication Using Deep Feature Learning. SACI 2021: 61-66 - [c33]Anita-Bella Réthi, Krisztian Antal Buza, Orsolya Máthé, Mátyás Fosztó, Tamás Koncz, Károly Simon:
medR: Software System for Managing Medical History and Patient Examination Data. SISY 2021: 61-66 - 2020
- [j11]Aleksandra Revina, Krisztián Búza, Vera G. Meister:
IT Ticket Classification: The Simpler, the Better. IEEE Access 8: 193380-193395 (2020) - [c32]Krisztián Búza, Aleksandra Revina:
Speeding up the SUCCESS Approach for Massive Industrial Datasets. INISTA 2020: 1-6 - [c31]Krisztián Búza:
ASTERICS: Projection-based Classification of EEG with Asymmetric Loss Linear Regression and Genetic Algorithm. SACI 2020: 35-40
2010 – 2019
- 2019
- [c30]Krisztián Búza, Tomás Horváth:
Factorization Machines for Blog Feedback Prediction. CORES 2019: 79-85 - [c29]Zakarya Farou, Krisztián Búza:
The Warping Window Size Effects the Accuracy of Person Identification based on Keystroke Dynamics. ITAT 2019: 84-89 - [c28]Dániel T. Várkonyi, Krisztián Búza:
Extreme Learning Machines with Regularization for the Classification of Gene Expression Data. ITAT 2019: 99-103 - [i3]Krisztián Búza:
Encouraging an Appropriate Representation Simplifies Training of Neural Networks. CoRR abs/1911.07245 (2019) - 2018
- [c27]Krisztián Búza:
Time Series Classification and its Applications. WIMS 2018: 4:1-4:4 - [p2]Annamária Szenkovits, Regina Meszlényi, Krisztián Búza, Noémi Gaskó, Rodica Ioana Lung, Mihai Suciu:
Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data. Advances in Feature Selection for Data and Pattern Recognition 2018: 185-202 - 2017
- [j10]Ladislav Peska, Krisztián Búza, Júlia Koller:
Drug-target interaction prediction: A Bayesian ranking approach. Comput. Methods Programs Biomed. 152: 15-21 (2017) - [j9]Regina Meszlényi, Krisztián Búza, Zoltán Vidnyánszky:
Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture. Frontiers Neuroinformatics 11: 61 (2017) - [j8]Krisztián Búza, Ladislav Peska:
Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression. Neurocomputing 260: 284-293 (2017) - [c26]Krisztián Búza, Piroska B. Kis:
Towards Privacy-Aware Keyboards. CORES 2017: 140-147 - [c25]Dóra Neubrandt, Krisztián Búza:
Projection-Based Person Identification. CORES 2017: 221-228 - [c24]Rajkumar Ramamurthy, Christian Bauckhage, Krisztián Búza, Stefan Wrobel:
Using Echo State Networks for Cryptography. ICANN (2) 2017: 663-671 - [c23]Krisztián Búza, Ladislav Peska:
ALADIN: A New Approach for Drug-Target Interaction Prediction. ECML/PKDD (2) 2017: 322-337 - [i2]Rajkumar Ramamurthy, Christian Bauckhage, Krisztián Búza, Stefan Wrobel:
Using Echo State Networks for Cryptography. CoRR abs/1704.01046 (2017) - [i1]Regina Meszlényi, Krisztián Búza, Zoltán Vidnyánszky:
Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. CoRR abs/1707.06682 (2017) - 2016
- [j7]Krisztián Búza, Noémi Ágnes Varga:
ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks. Appl. Artif. Intell. 30(6): 541-555 (2016) - [j6]Krisztián Búza:
Classification of gene expression data: A hubness-aware semi-supervised approach. Comput. Methods Programs Biomed. 127: 105-113 (2016) - [j5]Nenad Tomasev, Krisztián Búza, Dunja Mladenic:
Correcting the hub occurrence prediction bias in many dimensions. Comput. Sci. Inf. Syst. 13(1): 1-21 (2016) - [c22]Krisztián Búza:
Person Identification Based on Keystroke Dynamics: Demo and Open Challenge. CAiSE Forum 2016: 161-168 - [c21]Regina Meszlényi, Ladislav Peska, Viktor Gál, Zoltán Vidnyánszky, Krisztián Búza:
A Model for Classification Based on the Functional Connectivity Pattern Dynamics of the Brain. ENIC 2016: 203-208 - [c20]Regina Meszlényi, Ladislav Peska, Viktor Gál, Zoltán Vidnyánszky, Krisztián Búza:
Classification of fMRI data using dynamic time warping based functional connectivity analysis. EUSIPCO 2016: 245-249 - [c19]Rodica Ioana Lung, Mihai Suciu, Regina Meszlényi, Krisztián Búza, Noémi Gaskó:
Community Structure Detection for the Functional Connectivity Networks of the Brain. PPSN 2016: 633-643 - [c18]Krisztián Búza:
Drug-target interaction prediction with hubness-aware machine learning. SACI 2016: 437-440 - [c17]Krisztián Búza, Dóra Neubrandt:
How you type is who you are. SACI 2016: 453-456 - 2015
- [j4]Nenad Tomasev, Krisztián Búza:
Hubness-aware kNN classification of high-dimensional data in presence of label noise. Neurocomputing 160: 157-172 (2015) - [j3]Krisztián Búza, Alexandros Nanopoulos, Gábor I. Nagy:
Nearest neighbor regression in the presence of bad hubs. Knowl. Based Syst. 86: 250-260 (2015) - [c16]Krisztián Búza:
Semi-supervised Naive Hubness Bayesian k-Nearest Neighbor for Gene Expression Data. CORES 2015: 101-110 - [c15]Krisztián Búza, Júlia Koller, Kristóf Marussy:
PROCESS: Projection-Based Classification of Electroencephalograph Signals. ICAISC 2015: 91-100 - [p1]Nenad Tomasev, Krisztián Búza, Kristóf Marussy, Piroska B. Kis:
Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series. Feature Selection for Data and Pattern Recognition 2015: 231-262 - 2014
- [j2]Krisztián Búza, Gábor I. Nagy, Alexandros Nanopoulos:
Storage-optimizing clustering algorithms for high-dimensional tick data. Expert Syst. Appl. 41(9): 4148-4157 (2014) - [d1]Krisztián Búza:
BlogFeedback. UCI Machine Learning Repository, 2014
- 2013
- [c14]Kristóf Marussy, Krisztián Búza:
SUCCESS: A New Approach for Semi-supervised Classification of Time-Series. ICAISC (1) 2013: 437-447 - 2012
- [j1]Krisztián Búza, Alexandros Nanopoulos, Tomás Horváth, Lars Schmidt-Thieme:
GRAMOFON: General model-selection framework based on networks. Neurocomputing 75(1): 163-170 (2012) - [c13]Krisztián Búza:
Feedback Prediction for Blogs. GfKl 2012: 145-152 - [c12]Gábor I. Nagy, Krisztián Búza:
SOHAC: Efficient Storage of Tick Data That Supports Search and Analysis. ICDM 2012: 38-51 - 2011
- [b1]Krisztian Antal Buza:
Fusion Methods for Time-Series Classification. University of Hildesheim, 2011, ISBN 978-3-631-63085-3, pp. 1-144 - [c11]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
IQ estimation for accurate time-series classification. CIDM 2011: 216-223 - [c10]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Fusion of Similarity Measures for Time Series Classification. HAIS (2) 2011: 253-261 - [c9]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme, Júlia Koller:
Fast Classification of Electrocardiograph Signals via Instance Selection. HISB 2011: 9-16 - [c8]Krisztián Búza, Antal Buza, Piroska B. Kis:
A Distributed Genetic Algorithm for Graph-Based Clustering. ICMMI 2011: 323-331 - [c7]Timo Reuter, Philipp Cimiano, Lucas Drumond, Krisztián Búza, Lars Schmidt-Thieme:
Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. ICWSM 2011 - [c6]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. PAKDD (2) 2011: 149-160 - 2010
- [c5]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Time-Series Classification Based on Individualised Error Prediction. CSE 2010: 48-54 - [c4]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Individualized Error Estimation for Classification and Regression Models. GfKl 2010: 183-191 - [c3]Krisztián Búza, Alexandros Nanopoulos, Lars Schmidt-Thieme:
Graph-Based Model-Selection Framework for Large Ensembles. HAIS (1) 2010: 557-564
2000 – 2009
- 2008
- [c2]Krisztián Búza, Lars Schmidt-Thieme:
Motif-Based Classification of Time Series with Bayesian Networks and SVMs. GfKl 2008: 105-114 - [c1]Leandro Balby Marinho, Krisztián Búza, Lars Schmidt-Thieme:
Folksonomy-Based Collabulary Learning. ISWC 2008: 261-276
Coauthor Index
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last updated on 2024-05-08 23:46 CEST by the dblp team
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