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ORCIDChristian Borgelt
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- affiliation: Otto von Guericke University of Magdeburg, Germany
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2020 – today
- 2024
[c81]Felix Petersen, Aashwin Ananda Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin:
Uncertainty Quantification via Stable Distribution Propagation. ICLR 2024- [i10]Felix Petersen, Aashwin Ananda Mishra, Hilde Kuehne, Christian Borgelt, Oliver Deussen, Mikhail Yurochkin:
Uncertainty Quantification via Stable Distribution Propagation. CoRR abs/2402.08324 (2024) - 2023
- [c80]Fabian Berns, Georg Zimmermann, Christian Borgelt, Niclas Heilig, Jan Kirchhoff, Florian Stumpe:
Trustworthy Medical Operational AI: Marrying AI and Regulatory Requirements. IEEE Big Data 2023: 2700-2704 - [c79]Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen:
ISAAC Newton: Input-based Approximate Curvature for Newton's Method. ICLR 2023 - [i9]Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne
, Yuekai Sun, Oliver Deussen:
ISAAC Newton: Input-based Approximate Curvature for Newton's Method. CoRR abs/2305.00604 (2023) - 2022
- [b8]Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, Matthias Steinbrecher:
Computational Intelligence - A Methodological Introduction, Third Edition. Texts in Computer Science, Springer 2022, ISBN 978-3-030-42226-4, pp. 1-625 - [c78]Felix Petersen, Bastian Goldluecke, Christian Borgelt, Oliver Deussen:
GenDR: A Generalized Differentiable Renderer. CVPR 2022: 3992-4001 - [c77]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Monotonic Differentiable Sorting Networks. ICLR 2022 - [c76]Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen:
Differentiable Top-k Classification Learning. ICML 2022: 17656-17668 - [c75]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Deep Differentiable Logic Gate Networks. NeurIPS 2022 - [i8]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Monotonic Differentiable Sorting Networks. CoRR abs/2203.09630 (2022) - [i7]Felix Petersen, Bastian Goldluecke, Christian Borgelt, Oliver Deussen:
GenDR: A Generalized Differentiable Renderer. CoRR abs/2204.13845 (2022) - [i6]Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen:
Differentiable Top-k Classification Learning. CoRR abs/2206.07290 (2022) - [i5]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Deep Differentiable Logic Gate Networks. CoRR abs/2210.08277 (2022) - [i4]Michele Coscia, Christian Borgelt, Michael Szell:
Fast Multiplex Graph Association Rules for Link Prediction. CoRR abs/2211.12094 (2022) - 2021
- [c74]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision. ICML 2021: 8546-8555 - [c73]Victoria Racher, Christian Borgelt:
Gradient Ascent for Best Response Regression. IDA 2021: 141-154 - [c72]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Learning with Algorithmic Supervision via Continuous Relaxations. NeurIPS 2021: 16520-16531 - [i3]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision. CoRR abs/2105.04019 (2021) - [i2]Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen:
Learning with Algorithmic Supervision via Continuous Relaxations. CoRR abs/2110.05651 (2021) - 2020
- [b7]Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosaria Silipo:
Guide to Intelligent Data Science - How to Intelligently Make Use of Real Data, Second Edition. Texts in Computer Science, Springer 2020, ISBN 978-3-030-45573-6, pp. 1-328 - [c71]Christian Borgelt, Olha Yarikova:
Initializing k-means Clustering. DATA 2020: 260-267 - [c70]Christian Borgelt:
Even Faster Exact k-Means Clustering. IDA 2020: 93-105 - [p15]Christian Borgelt, Christian Braune, Rudolf Kruse:
Unsicheres, impräzises und unscharfes Wissen. Handbuch der Künstlichen Intelligenz 2020: 279-342
2010 – 2019
- 2019
- [j26]Inés Couso, Christian Borgelt, Eyke Hüllermeier, Rudolf Kruse:
Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Comput. Intell. Mag. 14(1): 31-44 (2019) - [c69]Juan M. Montoya, Christian Borgelt:
Wide and Deep Reinforcement Learning for Grid-based Action Games. ICAART (2) 2019: 50-59 - [c68]Juan M. Montoya, Christoph Doell, Christian Borgelt:
Wide and Deep Reinforcement Learning Extended for Grid-Based Action Games. ICAART (Revised Selected Papers) 2019: 224-245 - [c67]Iuliia Gavriushina, Oliver Sampson, Michael R. Berthold, Winfried Pohlmeier, Christian Borgelt:
Widened Learning of Index Tracking Portfolios. ICMLA 2019: 1800-1805 - [c66]Christoph Doell, Christian Borgelt:
Aggregation of Subclassifications: Methods, Tools and Experiments. SSCI 2019: 3124-3131 - [i1]Felix Petersen, Christian Borgelt, Oliver Deussen:
AlgoNet: C∞ Smooth Algorithmic Neural Networks. CoRR abs/1905.06886 (2019) - 2018
- [j25]Salatiel Ezennaya-Gomez, Christian Borgelt:
Mining Frequent Synchronous Patterns based on Item Cover Similarity. Int. J. Comput. Intell. Syst. 11(1): 526-539 (2018) - [c65]Christoph Doell, Sarah E. Donohue, Cedrik Pätz, Christian Borgelt:
Training Neural Networks to Distinguish Craving Smokers, Non-craving Smokers, and Non-smokers. IDA 2018: 75-86 - [c64]Oliver R. Sampson, Christian Borgelt, Michael R. Berthold:
Communication-Free Widened Learning of Bayesian Network Classifiers Using Hashed Fiedler Vectors. IDA 2018: 264-277 - [c63]Patrick Winter, Christian Borgelt, Michael R. Berthold:
Learned Feature Generation for Molecules. IDA 2018: 380-391 - [c62]Christoph Doell, Sarah E. Donohue, Christian Borgelt:
Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals. SSCI 2018: 510-517 - 2016
- [b6]Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, Matthias Steinbrecher:
Computational Intelligence - A Methodological Introduction, Second Edition. Texts in Computer Science, Springer 2016, ISBN 978-1-4471-7294-9, pp. 1-551 - [j24]Kristian Loewe, Sarah E. Donohue, Mircea Ariel Schoenfeld, Rudolf Kruse, Christian Borgelt:
Memory-Efficient Analysis of Dense Functional Connectomes. Frontiers Neuroinformatics 10: 50 (2016) - [c61]Christian Borgelt, Rudolf Kruse:
Agglomerative Fuzzy Clustering. SMPS 2016: 69-77 - [p14]Christian Borgelt, David Picado-Muiño:
Significant Frequent Item Sets Via Pattern Spectrum Filtering. Fuzzy Technology 2016: 73-84 - 2015
- [j23]David Picado-Muiño, Christian Borgelt:
Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains. Comput. Intell. Neurosci. 2015: 427829:1-427829:12 (2015) - [j22]Christian Borgelt, Rudolf Kruse:
Bedeutung von Zugehörigkeitsgraden in der Fuzzy-Technologie. Inform. Spektrum 38(6): 490-499 (2015) - [c60]Christian Borgelt, Christian Braune, Kristian Loewe, Rudolf Kruse:
Mining Frequent Parallel Episodes with Selective Participation. IFSA-EUSFLAT 2015 - [c59]Salatiel Ezennaya-Gomez, Christian Borgelt:
Mining Frequent Synchronous Patterns with a Graded Notion of Synchrony. IFSA-EUSFLAT 2015 - [c58]David Picado-Muiño, Christian Borgelt:
Automatic learning of synchrony in neuronal electrode recordings. IFSA-EUSFLAT 2015 - [c57]Salatiel Ezennaya-Gomez, Christian Borgelt:
Mining Significant Frequent Patterns in Parallel Episodes with a Graded Notion of Synchrony and Selective Participation. IJCCI (NCTA) 2015: 39-48 - [c56]Salatiel Ezennaya-Gomez, Christian Borgelt, Christian Braune, Kristian Loewe, Rudolf Kruse:
Handling Selective Participation in Neuron Assembly Detection. IJCCI (Selected Papers) 2015: 386-406 - [p13]Christian Borgelt, Christian Braune, Marie-Jeanne Lesot, Rudolf Kruse:
Handling Noise and Outliers in Fuzzy Clustering. Fifty Years of Fuzzy Logic and its Applications 2015: 315-335 - 2014
- [j21]David Picado-Muiño, Christian Borgelt:
Frequent item set mining for sequential data: Synchrony in neuronal spike trains. Intell. Data Anal. 18(6): 997-1012 (2014) - [j20]David Picado-Muiño, Iván Castro León, Christian Borgelt:
Fuzzy characterization of spike synchrony in parallel spike trains. Soft Comput. 18(1): 71-83 (2014) - [c55]Christian Borgelt, David Picado-Muiño:
Simple Pattern Spectrum Estimation for Fast Pattern Filtering with CoCoNAD. IDA 2014: 37-48 - 2013
- [b5]Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held:
Computational Intelligence - A Methodological Introduction. Texts in Computer Science, Springer 2013, ISBN 978-1-4471-5012-1, pp. I-XI, 1-490 - [j19]Emiliano Torre, David Picado-Muiño, Michael Denker, Christian Borgelt, Sonja Grün:
Statistical evaluation of synchronous spike patterns extracted by frequent item set mining. Frontiers Comput. Neurosci. 7: 132 (2013) - [j18]David Picado-Muiño, Christian Borgelt, Denise Berger, George L. Gerstein, Sonja Grün:
Finding neural assemblies with frequent item set mining. Frontiers Neuroinformatics 7: 9 (2013) - [c54]Christian Borgelt, Christian Braune:
Prototype Construction for Clustering of Point Processes based on Imprecise Synchrony. EUSFLAT Conf. 2013 - [c53]Anja Bachmann, Christian Borgelt, Gyözö Gidófalvi:
Incremental Frequent Route Based Trajectory Prediction. CTS@SIGSPATIAL 2013: 49 - [c52]Christian Borgelt, David Picado-Muiño:
Finding Frequent Patterns in Parallel Point Processes. IDA 2013: 116-126 - [c51]Christian Braune, Christian Borgelt, Rudolf Kruse:
Behavioral Clustering for Point Processes. IDA 2013: 127-137 - [p12]Christian Borgelt, Christian Braune, Heiko Timm, Rudolf Kruse:
Unsicheres und vages Wissen. Handbuch der Künstlichen Intelligenz 2013: 235-296 - 2012
- [j17]Christian Borgelt, Christian Braune, Tobias Kötter, Sonja Grün:
New algorithms for finding approximate frequent item sets. Soft Comput. 16(5): 903-917 (2012) - [j16]Christian Borgelt:
Frequent item set mining. WIREs Data Mining Knowl. Discov. 2(6): 437-456 (2012) - [c50]Christian Braune, Christian Borgelt, Sonja Grün:
Assembly Detection in Continuous Neural Spike Train Data. IDA 2012: 78-89 - [c49]David Picado-Muiño, Iván Castro León, Christian Borgelt:
Fuzzy Frequent Pattern Mining in Spike Trains. IDA 2012: 289-300 - [c48]Christian Borgelt:
Soft Pattern Mining in Neuroscience. SMPS 2012: 3-10 - [p11]Christian Borgelt:
Network Creation: Overview. Bisociative Knowledge Discovery 2012: 51-53 - [p10]Marc Segond, Christian Borgelt:
Selecting the Links in BisoNets Generated from Document Collections. Bisociative Knowledge Discovery 2012: 54-65 - [p9]Marc Segond, Christian Borgelt:
Cover Similarity Based Item Set Mining. Bisociative Knowledge Discovery 2012: 104-121 - 2011
- [c47]Christian Borgelt, Xiaoyuan Yang, Rubén Nogales-Cadenas, Pedro Carmona-Saez, Alberto D. Pascual-Montano:
Finding closed frequent item sets by intersecting transactions. EDBT 2011: 367-376 - [c46]Gyözö Gidófalvi, Manohar Kaul, Christian Borgelt, Torben Bach Pedersen:
Frequent route based continuous moving object location- and density prediction on road networks. GIS 2011: 381-384 - [c45]Christian Borgelt, Tobias Kötter:
Mining Fault-Tolerant Item Sets Using Subset Size Occurrence Distributions. IDA 2011: 43-54 - [c44]Christian Braune, Christian Borgelt, Sonja Grün:
Finding Ensembles of Neurons in Spike Trains by Non-linear Mapping and Statistical Testing. IDA 2011: 55-66 - [c43]Marc Segond, Christian Borgelt:
Item Set Mining Based on Cover Similarity. PAKDD (2) 2011: 493-505 - 2010
- [b4]Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn:
Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data. Texts in Computer Science 42, Springer 2010, ISBN 978-1-84882-259-7, pp. I-XIII, 1-394 - [j15]Denise Berger, Christian Borgelt, Sebastien Louis, Abigail Morrison, Sonja Grün:
Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains. Comput. Intell. Neurosci. 2010: 439648:1-439648:18 (2010) - [j14]Christian Borgelt:
A conditional independence algorithm for learning undirected graphical models. J. Comput. Syst. Sci. 76(1): 21-33 (2010) - [j13]Sebastien Louis, Christian Borgelt, Sonja Grün:
Complexity distribution as a measure for assembly size and temporal precision. Neural Networks 23(6): 705-712 (2010) - [c42]Marc Segond, Christian Borgelt:
Selecting the Links in BisoNets Generated from Document Collections. IDA 2010: 196-207 - [p8]Christian Borgelt:
Simple Algorithms for Frequent Item Set Mining. Advances in Machine Learning II 2010: 351-369 - [e3]Christian Borgelt, Gil González-Rodríguez, Wolfgang Trutschnig, María Asunción Lubiano, María Ángeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz:
Combining Soft Computing and Statistical Methods in Data Analysis, SMPS 2010, Oviedo, Spain, September 29 - October 1, 2010. Advances in Intelligent and Soft Computing 77, Springer 2010, ISBN 978-3-642-14745-6 [contents]
2000 – 2009
- 2009
- [j12]Christian Borgelt:
Accelerating fuzzy clustering. Inf. Sci. 179(23): 3985-3997 (2009) - [c41]Christian Borgelt, Xiaomeng Wang:
SaM: A Split and Merge Algorithm for Fuzzy Frequent Item Set Mining. IFSA/EUSFLAT Conf. 2009: 968-973 - [c40]Sonja Grün, Denise Berger, Christian Borgelt:
Identification of neurons participating in cell assemblies. ICASSP 2009: 3493-3496 - [p7]Christian Borgelt, Xiaomeng Wang:
(Approximate) Frequent Item Set Mining Made Simple with a Split and Merge Algorithm. Scalable Fuzzy Algorithms for Data Management and Analysis 2009: 254-272 - [p6]Christian Borgelt, Thorsten Meinl:
Full Perfect Extension Pruning for Frequent Subgraph Mining. Mining Complex Data 2009: 189-205 - 2008
- [j11]Magdalene G. Borgelt, Christian Borgelt, Christos Levcopoulos:
Fixed Parameter Algorithms for the Minimum Weight Triangulation Problem. Int. J. Comput. Geom. Appl. 18(3): 185-220 (2008) - [c39]Christian Borgelt:
Feature weighting and feature selection in fuzzy clustering. FUZZ-IEEE 2008: 838-844 - [c38]Christian Borgelt:
Fuzzy Subspace Clustering. GfKl 2008: 93-103 - 2007
- [j10]Christian Borgelt:
Resampling for Fuzzy Clustering. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 15(5): 595-614 (2007) - [c37]Christian Borgelt:
Prototype-less Fuzzy Clustering. FUZZ-IEEE 2007: 1-6 - [c36]Christian Borgelt:
Learning Undirected Possibilistic Networks with Conditional Independence Tests. FUZZ-IEEE 2007: 1-6 - [c35]Christian Borgelt, Gil Gonzáles-Rodríguez:
FrIDA - A Free Intelligent Data Analysis Toolbox. FUZZ-IEEE 2007: 1-5 - [c34]Christian Borgelt, Mathias Fiedler:
Graph Mining: Repository vs. Canonical Form. GfKl 2007: 229-236 - [c33]Mathias Fiedler, Christian Borgelt:
Subgraph Support in a Single Large Graph. ICDM Workshops 2007: 399-404 - [c32]Mathias Fiedler, Christian Borgelt:
Support Computation for Mining Frequent Subgraphs in a Single Graph. MLG 2007 - 2006
- [c31]Christian Borgelt, Rudolf Kruse:
Finding the Number of Fuzzy Clusters by Resampling. FUZZ-IEEE 2006: 48-54 - [c30]Christian Borgelt:
Canonical Forms for Frequent Graph Mining. GfKl 2006: 337-349 - [c29]Christian Borgelt, Thorsten Meinl:
Full Perfect Extension Pruning for Frequent Graph Mining. ICDM Workshops 2006: 263-268 - [e2]Myra Spiliopoulou, Rudolf Kruse, Christian Borgelt, Andreas Nürnberger, Wolfgang Gaul:
From Data and Information Analysis to Knowledge Engineering, Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Magdeburg, March 9-11, 2005. Studies in Classification, Data Analysis, and Knowledge Organization, Springer 2006, ISBN 978-3-540-31313-7 [contents] - 2005
- [c28]Christian Borgelt, Rudolf Kruse:
Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuits. ECSQARU 2005: 100-110 - [c27]Christian Borgelt, Michael R. Berthold, David E. Patterson:
Molecular Fragment Mining for Drug Discovery. ECSQARU 2005: 1002-1013 - [c26]Christian Borgelt, Andreas Nürnberger, Rudolf Kruse:
Fuzzy Learning Vector Quantization with Size and Shape Parameters. FUZZ-IEEE 2005: 195-200 - [c25]Christian Döring, Christian Borgelt, Rudolf Kruse:
Effects of Irrelevant Attributes in Fuzzy Clustering. FUZZ-IEEE 2005: 862-866 - [c24]Xiaomeng Wang, Christian Borgelt, Rudolf Kruse:
Fuzzy frequent pattern discovering based on recursive elimination. ICMLA 2005 - [c23]Magdalene Grantson, Christian Borgelt, Christos Levcopoulos:
Minimum Weight Triangulation by Cutting Out Triangles. ISAAC 2005: 984-994 - 2004
- [j9]Christian Borgelt, Rudolf Kruse:
Probabilistische grafische Modellle und ihre Anwendung in der Automobilindustrie. Datenbank-Spektrum 9: 18-23 (2004) - [j8]Heiko Timm, Christian Borgelt, Christian Döring, Rudolf Kruse:
An extension to possibilistic fuzzy cluster analysis. Fuzzy Sets Syst. 147(1): 3-16 (2004) - [j7]Heiko Hofer, Christian Borgelt, Michael R. Berthold:
Large scale mining of molecular fragments with wildcards. Intell. Data Anal. 8(5): 495-504 (2004) - [c22]Christian Borgelt:
Recursion Pruning for the Apriori Algorithm. FIMI 2004 - [c21]Xiaomeng Wang, Christian Borgelt:
Information measures in fuzzy decision trees. FUZZ-IEEE 2004: 85-90 - [c20]Christian Borgelt, Daniela Girimonte, Giuseppe Acciani:
Modeling and diagnosis of analog circuits with probabilistic graphical models. ISCAS (4) 2004: 485-488 - [c19]Christian Borgelt, Daniela Girimonte, Giuseppe Acciani:
Learning vector quantization: cluster size and cluster number. ISCAS (5) 2004: 808-811 - [c18]Christian Borgelt, Andreas Nürnberger:
Experiments in Term Weighting and Keyword Extraction in Document Clustering. LWA 2004: 123-130 - [c17]Christian Borgelt, Rudolf Kruse:
Shape and Size Regularization in Expectation Maximization and Fuzzy Clustering. PKDD 2004: 52-62 - [c16]Christian Borgelt, Thorsten Meinl, Michael R. Berthold:
Advanced pruning strategies to speed up mining closed molecular fragments. SMC (5) 2004: 4565-4570 - 2003
- [b3]Christian Borgelt, Frank Klawonn, Rudolf Kruse, Detlef D. Nauck:
Neuro-Fuzzy-Systeme. Vieweg 2003, ISBN 978-3-528-25265-6, pp. 1-396 - [j6]Christian Borgelt, Rudolf Kruse:
Operations and evaluation measures for learning possibilistic graphical models. Artif. Intell. 148(1-2): 385-418 (2003) - [j5]Rudolf Kruse, Christian Borgelt:
Information mining. Int. J. Approx. Reason. 32(2-3): 63-65 (2003) - [j4]Christian Borgelt, Rudolf Kruse:
Learning possibilistic graphical models from data. IEEE Trans. Fuzzy Syst. 11(2): 159-172 (2003) - [c15]Christian Borgelt, Rudolf Kruse:
Speeding up fuzzy clustering with neural network techniques. FUZZ-IEEE 2003: 852-856 - [c14]Heiko Hofer, Christian Borgelt, Michael R. Berthold:
Large Scale Mining of Molecular Fragments with Wildcards. IDA 2003: 376-385 - [c13]Christian Borgelt:
On Identifying Tree-Structured Perfect Maps. KI 2003: 385-395 - [p5]Christian Borgelt, Heiko Timm, Rudolf Kruse:
Unsicheres und vages Wissens. Handbuch der Künstlichen Intelligenz 2003: 290-347 - [p4]Christian Borgelt, Rudolf Kruse:
Local Structure Learning in Graphical Models. Planning Based on Decision Theory 2003: 99-118 - [e1]Michael R. Berthold, Hans-Joachim Lenz, Elizabeth Bradley, Rudolf Kruse, Christian Borgelt:
Advances in Intelligent Data Analysis V, 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003, Proceedings. Lecture Notes in Computer Science 2810, Springer 2003, ISBN 3-540-40813-4 [contents] - 2002
- [b2]Christian Borgelt, Rudolf Kruse:
Graphical models - methods for data analysis and mining. Wiley 2002, ISBN 978-0-470-84337-6, pp. I-VIII, 1-358 - [c12]Rudolf Kruse, Christian Borgelt:
Data Mining with Graphical Models. ALT 2002: 22 - [c11]Christian Borgelt, Rudolf Kruse:
Induction of Association Rules: Apriori Implementation. COMPSTAT 2002: 395-400 - [c10]Rudolf Kruse, Christian Borgelt:
Data Mining with Graphical Models. Discovery Science 2002: 2-11 - [c9]Christian Borgelt, Michael R. Berthold:
Mining Molecular Fragments: Finding Relevant Substructures of Molecules. ICDM 2002: 51-58 - 2001
- [j3]Christian Borgelt, Rudolf Kruse:
Unsicherheit und Vagheit: Begriffe, Methoden, Forschungsthemen. Künstliche Intell. 15(3): 5-8 (2001) - [j2]Christian Borgelt, Rudolf Kruse:
Unsicherheit und Vagheit - Serviceteil. Künstliche Intell. 15(3): 48 (2001) - [c8]Christian Borgelt, Rudolf Kruse:
An Empirical Investigation of the K2 Metric. ECSQARU 2001: 240-251 - [c7]Christian Borgelt, Rudolf Kruse:
Learning Graphical Models With Hypertree Structure Using a Simulated Annealing Approach. FUZZ-IEEE 2001: 135-138 - 2000
- [b1]Christian Borgelt:
Data mining with graphical models. Otto-von-Guericke University Magdeburg, Germany, 2000, pp. 1-366 - [c6]Christian Borgelt, Heiko Timm, Rudolf Kruse:
Using fuzzy clustering to improve naive Bayes classifiers and probabilistic networks. FUZZ-IEEE 2000: 53-58 - [c5]Rudolf Kruse, Christian Borgelt, Detlef D. Nauck:
Problems and Prospects in Fuzzy Data Analysis. Intelligent Systems and Soft Computing 2000: 95-109 - [c4]Christian Borgelt, Heiko Timm:
Advanced Fuzzy Clustering and Decision Tree Plug-Ins for Data EngineTM. Intelligent Systems and Soft Computing 2000: 188-212 - [p3]Christian Borgelt, Jörg Gebhardt, Rudolf Kruse:
Possibilistic Graphical Models. Computational Intelligence in Data Mining 2000: 51-67 - [p2]Christian Borgelt:
Data mining with graphical models. Ausgezeichnete Informatikdissertationen 2000: 21-30
1990 – 1999
- 1999
- [c3]Christian Borgelt, Rudolf Kruse:
A Critique of Inductive Causation. ESCQARU 1999: 68-79 - [p1]Christian Borgelt, Jörg Gebhardt, Rudolf Kruse:
Fuzzy Methoden in der Datenanalyse. Fuzzy Theorie und Stochastik 1999: 370-386 - 1998
- [j1]Christian Borgelt, Rudolf Kruse, Guido Lindner:
Lernen probabilistischer und possibilistischer Netze aus Daten: Theorie und Anwendung. Künstliche Intell. 12(1): 11-17 (1998) - [c2]Rudolf Kruse, Christian Borgelt:
Data Mining with Graphical Models. KI 1998: 3-16 - 1997
- [c1]Christian Borgelt, Rudolf Kruse:
Some Experimental Results on Learning Probabilistic and Possibilistic Networks with Different Evaluation Measures. ECSQARU-FAPR 1997: 71-85
Coauthor Index
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