C. Borgelt

发表

Guided Incremental Construction of Belief Networks

R. Kruse, H. Lenz, M. Berthold, 2003 .

The Hubness Phenomenon: Fact or Artifact?

C. Borgelt, Thomas Low, S. Stober, 2013 .

EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT

Christian Borgelt, C. Borgelt, 2003 .

Abductive Inference with Probabilistic Graphical Models

C. Borgelt, 2008 .

INFERENCE WITH PROBABILISTIC NETWORKS

R. Kruse, C. Borgelt, Abductive, 2004 .

Objective Functions for Fuzzy Clustering

C. Borgelt, 2013 .

An Extended Objective Function for Prototype-less Fuzzy Clustering

R. Kruse, C. Borgelt, 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

Fuzzy and Probabilistic Clustering with Shape and Size Constraints

R. Kruse, C. Borgelt, 2005 .

Fuzzy clustering of quantitative and qualitative data

R. Kruse, C. Borgelt, C. Doring, 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

Fuzzy Cluster Analysis with Cluster Repulsion

R. Kruse, C. Borgelt, H. Timm, 2001 .

The Role of Soft Computing in Intelligent Data Analysis ( Invited Paper )

Nees Jan van Eck, D. Nauck, R. Kruse, 2008 .

Fuzzy Frequent Item Set Mining based on Recursive Elimination

R. Kruse, C. Borgelt, Xiaomeng Wang, 2006 .

Widespread temporo-occipital lobe dysfunction in amyotrophic lateral sclerosis

Joseph A. Harris, M. Schoenfeld, H. Heinze, 2017, Scientific Reports.

Subgraph Support in a Single Large Graph

Christian Borgelt, Mathias Fiedler, C. Borgelt, 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

Fixed Parameter Algorithms for Minimum Weight Partitions

C. Borgelt, C. Levcopoulos, Magdalene Grantson, 2006 .

A Fixed Parameter Algorithm for Minimum Weight Triangulation: Analysis and Experiments

Christian Borgelt, Christos Levcopoulos, Magdalene Grantson, 2005 .

Mining Fuzzy Frequent Item Sets

Christian Borgelt, Rudolf Kruse, R. Kruse, 2005 .

Possibilistic Networks with Local Structure

R. Kruse, C. Borgelt, 2004 .

Possibilistic Networks : Data Mining Applications

R. Kruse, C. Borgelt, 2004 .

Probabilistic and possibilistic networks and how to learn them from data

R. Kruse, C. Borgelt, 1998 .

Evaluation measures for learning probabilistic and possibilistic networks

C. Borgelt, C. Borgelt, R. Kruse, 1997, Proceedings of 6th International Fuzzy Systems Conference.

GenDR: A Generalized Differentiable Renderer

C. Borgelt, O. Deussen, Bastian Goldluecke, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Data Mining with Fuzzy Methods : Status and Perspectives

D. Nauck, R. Kruse, C. Borgelt, 1999 .

A Naive Bayes Style Possibilistic Classifier

C. Borgelt, J. Gebhardt, 2004 .

“ BisoNet ” Generation using Textual Data

C. Borgelt, M. Segond, marc. segond, 2009 .

Experiments in Document Clustering using Cluster Specific Term Weights

C. Borgelt, A. Nürnberger, 1982 .

Introduction to Graphical Models and How to Learn Them from Data

C. Borgelt, 2005 .

Notes on the Dynamic Bichromatic All-Nearest-Neighbors Problem

Magdalene G. Borgelt, C. Borgelt, 2008 .

Differentiable Top-k Classification Learning

C. Borgelt, O. Deussen, Felix Petersen, 2022, International Conference on Machine Learning.

Data Preparation

F. Klawonn, F. Höppner, M. Berthold, 2020, Texts in Computer Science.

Guide to Intelligent Data Science: How to Intelligently Make Use of Real Data

Frank Klawonn, Michael R. Berthold, Frank Höppner, 2020, Texts in Computer Science.

Fuzzy Computation in Practice

Sadaaki Miyamoto, Waldemar Karwowski, Piero P. Bonissone, 1998 .

Computational Intelligence

Christian Borgelt, Rudolf Kruse, Sanaz Mostaghim, 2016, Texts in Computer Science.

Chapter F1. 2: Inference Methods

Christian Borgelt, Rudolf Kruse, Jörg Gebhardt, 1998 .

Gradient Ascent for Best Response Regression

Christian Borgelt, Victoria Racher, C. Borgelt, 2021, IDA.

Appendix B: Software Tools

Christian Borgelt, Rudolf Kruse, Matthias Steinbrecher, 2009 .

Learning with Algorithmic Supervision via Continuous Relaxations pdf

Christian Borgelt, Oliver Deussen, Felix Petersen, 2021, NeurIPS.

Deep Differentiable Logic Gate Networks

C. Borgelt, O. Deussen, Felix Petersen, 2022, NeurIPS.

Full Perfect Extension Pruning for Frequent Subgraph Mining

Christian Borgelt, Thorsten Meinl, C. Borgelt, 2009, Mining Complex Data.

Project Understanding

F. Klawonn, F. Höppner, M. Berthold, 2020, Texts in Computer Science.

New algorithms for finding approximate frequent item sets

C. Borgelt, T. Kötter, Christian Braune, 2011, Soft Computing.

Fast Multiplex Graph Association Rules for Link Prediction

C. Borgelt, M. Szell, M. Coscia, 2022, ArXiv.

Fuzzy characterization of spike synchrony in parallel spike trains

C. Borgelt, David Picado Muiño, Iván Castro León, 2013, Soft Computing.

Fast construction of voxel-level functional connectivity graphs

R. Kruse, C. Borgelt, M. Grueschow, 2014, BMC Neuroscience.

Introduction to Neural Networks

Christian Borgelt, Rudolf Kruse, Sanaz Mostaghim, 2016 .

Finding Patterns

F. Klawonn, F. Höppner, M. Berthold, 2020, Texts in Computer Science.

Handling Noise and Outliers in Fuzzy Clustering

Christian Borgelt, Rudolf Kruse, Christian Braune, 2015, Fifty Years of Fuzzy Logic and its Applications.

Prototype Construction for Clustering of Point Processes based on Imprecise Synchrony

Christian Borgelt, Christian Braune, C. Borgelt, 2013, EUSFLAT Conf..

Radial Basis Function Networks

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Resampling for Fuzzy Clustering

Christian Borgelt, C. Borgelt, 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

FrIDA -A Free Intelligent Data Analysis Toolbox

Christian Borgelt, Gil Gonzáles-Rodríguez, C. Borgelt, 2007, 2007 IEEE International Fuzzy Systems Conference.

Prototype-less Fuzzy Clustering

Christian Borgelt, C. Borgelt, 2007, 2007 IEEE International Fuzzy Systems Conference.

FrIDA A FreeIntelligent DataAnalysis Toolbox

Christian Borgelt, C. Borgelt, 2007 .

Finding the Number of Fuzzy Clusters by Resampling

Christian Borgelt, Rudolf Kruse, R. Kruse, 2006, 2006 IEEE International Conference on Fuzzy Systems.

Learning vector quantization: cluster size and cluster number

Christian Borgelt, Giuseppe Acciani, Daniela Girimonte, 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

Constraining Shape and Size in Clustering

Christian Borgelt, Rudolf Kruse, R. Kruse, 2009 .

Shape and Size Regularization in Expectation Maximization and Fuzzy Clustering

Christian Borgelt, Rudolf Kruse, R. Kruse, 2004, PKDD.

Problems and Prospects in Fuzzy Data Analysis

Christian Borgelt, Rudolf Kruse, Detlef D. Nauck, 2000, Intelligent Systems and Soft Computing.

Fuzzy Methoden in der Datenanalyse

Rudolf Kruse, Jörg Gebhardt, Christian Borgelt, 1999, Fuzzy Theorie und Stochastik.

The Extension Principle

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Possibilistic Graphical Models

Christian Borgelt, Rudolf Kruse, Jörg Gebhardt, 2000, Computational Intelligence in Data Mining.

Data mining with graphical models

Christian Borgelt, Rudolf Kruse, C. Borgelt, 1998, Ausgezeichnete Informatikdissertationen.

Frequent item set mining

Christian Borgelt, C. Borgelt, 2012, WIREs Data Mining Knowl. Discov..

Fundamental Evolutionary Algorithms

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Cover Similarity Based Item Set Mining

Christian Borgelt, Marc Segond, C. Borgelt, 2012, Bisociative Knowledge Discovery.

Item Set Mining Based on Cover Similarity

Christian Borgelt, Marc Segond, C. Borgelt, 2011, PAKDD.

MoSS: a program for molecular substructure mining

Christian Borgelt, Thorsten Meinl, Michael Berthold, 2005 .

Communication-Free Widened Learning of Bayesian Network Classifiers Using Hashed Fiedler Vectors

Christian Borgelt, Michael R. Berthold, Oliver R. Sampson, 2018, IDA.

Learning possibilistic graphical models from data

Christian Borgelt, Rudolf Kruse, R. Kruse, 2003, IEEE Trans. Fuzzy Syst..

A Critique of Inductive Causation

Christian Borgelt, Rudolf Kruse, R. Kruse, 1999, ESCQARU.

Frontiers in Computational Intelligence

Christian Borgelt, Andreas Nürnberger, Sanaz Mostaghim, 2018 .

Monotonic Differentiable Sorting Networks

C. Borgelt, O. Deussen, Felix Petersen, 2022, ICLR.

Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision

Christian Borgelt, Oliver Deussen, Felix Petersen, 2021, ICML.

A framework for discovering interesting business changes from data

Christian Borgelt, Rudolf Kruse, Detlef Nauck, 2006 .

Dependence Relationships between Gene Ontology Terms based on TIGR Gene Product Annotations

Barry Smith, Anand Kumar, Christian Borgelt, 2004 .

Computational Intelligence

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013, Texts in Computer Science.

Abductive inference with probabilistic networks

Christian Borgelt, Rudolf Kruse, R. Kruse, 2000 .

Aggregation of Subclassifications: Methods, Tools and Experiments

Christoph Doell, Christian Borgelt, C. Borgelt, 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

Introduction to Fuzzy Sets and Fuzzy Logic

Christian Borgelt, Rudolf Kruse, Sanaz Mostaghim, 2016 .

Fuzzy Sets and Fuzzy Logic

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Using fuzzy clustering to improve naive Bayes classifiers and probabilistic networks

Heiko Timm, Rudolf Kruse, Christian Borgelt, 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

Operations and evaluation measures for learning possibilistic graphical models

Christian Borgelt, Rudolf Kruse, R. Kruse, 2003, Artif. Intell..

Special Applications and Techniques

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Elements of Evolutionary Algorithms

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Support Computation for Mining Frequent Subgraphs in a Single Graph

Christian Borgelt, Mathias Fiedler, C. Borgelt, 2007, MLG.

Large scale mining of molecular fragments with wildcards

Christian Borgelt, Michael R. Berthold, Heiko Hofer, 2004, Intell. Data Anal..

Mining molecular fragments: finding relevant substructures of molecules

Christian Borgelt, Michael R. Berthold, M. Berthold, 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

An implementation of the FP-growth algorithm

Christian Borgelt, C. Borgelt, 2005 .

A Naive Bayes Style Possibilistic Classifier

C. Borgelt, J. Gebhardt, 2004 .

Full Perfect Extension Pruning for Frequent Graph Mining

Christian Borgelt, Thorsten Meinl, C. Borgelt, 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning

Eyke Hüllermeier, Christian Borgelt, Rudolf Kruse, 2019, IEEE Computational Intelligence Magazine.

Local Structure Learning in Graphical Models

Christian Borgelt, Rudolf Kruse, R. Kruse, 2003, Planning Based on Decision Theory.

Keeping things simple: finding frequent item sets by recursive elimination

Christian Borgelt, C. Borgelt, 2005 .

An extension to possibilistic fuzzy cluster analysis

R. Kruse, C. Borgelt, Christian Döring, 2004, Fuzzy Sets Syst..

Speeding up fuzzy clustering with neural network techniques

Christian Borgelt, Rudolf Kruse, R. Kruse, 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

Probalistic Networks and Fuzzy Clustering as Generalizations of Naive Bayes Classifiers

Heiko Timm, Christian Borgelt, Rudolf Kruse, 2001 .

Advanced Fuzzy Clustering and Decision Tree Plug-Ins for Data EngineTM

Heiko Timm, Christian Borgelt, C. Borgelt, 2000, Intelligent Systems and Soft Computing.

Statistical evaluation of synchronous spike patterns extracted by frequent item set mining

Christian Borgelt, Sonja Grün, Michael Denker, 2013, Front. Comput. Neurosci..

Finding neural assemblies with frequent item set mining

Christian Borgelt, Sonja Grün, George L. Gerstein, 2013, Front. Neuroinform..

Induction of Association Rules: Apriori Implementation

Christian Borgelt, Rudolf Kruse, R. Kruse, 2002, COMPSTAT.

Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuits

Christian Borgelt, Rudolf Kruse, R. Kruse, 2005, ECSQARU.

Modeling and diagnosis of analog circuits with probabilistic graphical models

Christian Borgelt, Giuseppe Acciani, Daniela Girimonte, 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

Fuzzy data analysis: challenges and perspectives

D. Nauck, R. Kruse, C. Borgelt, 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

Selecting appropriate surrogate methods for spike correlation analysis

Sonja Grün, Markus Diesmann, Sebastien Louis, 2010, BMC Neuroscience.

Frequent route based continuous moving object location- and density prediction on road networks

Christian Borgelt, Torben Bach Pedersen, Manohar Kaul, 2011, GIS.

ISAAC Newton: Input-based Approximate Curvature for Newton's Method

Dongsung Huh, Yuekai Sun, C. Borgelt, 2023, ICLR.

Graphical models - methods for data analysis and mining

Christian Borgelt, Rudolf Kruse, R. Kruse, 2002 .

Memory-Efficient Analysis of Dense Functional Connectomes

Mircea Ariel Schoenfeld, Christian Borgelt, Rudolf Kruse, 2016, Front. Neuroinform..

Learning Probabilistic and Possibilistic Networks : Theory and Applications

Christian Borgelt, Rudolf Kruse, R. Kruse, 1995 .

Mining Fault-Tolerant Item Sets Using Subset Size Occurrence Distributions

Christian Borgelt, Tobias Kötter, C. Borgelt, 2011, IDA.

Finding closed frequent item sets by intersecting transactions

Christian Borgelt, Alberto D. Pascual-Montano, Xiaoyuan Yang, 2011, EDBT/ICDT '11.

Computational Swarm Intelligence

Christian Borgelt, Rudolf Kruse, Sanaz Mostaghim, 2016 .

Selecting the Links in BisoNets Generated from Document Collections

Christian Borgelt, Marc Segond, C. Borgelt, 2010, IDA.

Recurrent Networks

R. Kruse, C. Borgelt, Sanaz Mostaghim, 2021, Deep Learning in Science.

Graph Mining : An Overview

Christian Borgelt, C. Borgelt, 2009 .

New algorithms for finding approximate frequent item sets

Christian Borgelt, Sonja Grün, Christian Braune, 2012, Soft Comput..

Significant Frequent Item Sets Via Pattern Spectrum Filtering

Christian Borgelt, David Picado-Muiño, C. Borgelt, 2016, Fuzzy Technology.

Simple Algorithms for Frequent Item Set Mining

Christian Borgelt, C. Borgelt, 2010, Advances in Machine Learning II.

(Approximate) Frequent Item Set Mining Made Simple with a Split and Merge Algorithm

Christian Borgelt, Xiaomeng Wang, C. Borgelt, 2009, Scalable Fuzzy Algorithms for Data Management and Analysis.

SaM: A Split and Merge Algorithm for Fuzzy Frequent Item Set Mining

Christian Borgelt, Xiaomeng Wang, C. Borgelt, 2009, IFSA/EUSFLAT Conf..

Threshold Logic Units

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Generalisability of sleep stage classification based on interbeat intervals: validating three machine learning approaches on self-recorded test data

Dominik P. J. Heib, C. Borgelt, C. Kranzinger, 2023, Behaviormetrika.

Improving naive Bayes classifiers using neuro-fuzzy learning

A. Nurnberger, C. Borgelt, A. Klose, 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

Multi-Layer Perceptrons

Frank Klawonn, Christian Borgelt, Rudolf Kruse, 2013 .

Computational Intelligence: A Methodological Introduction

Frank Klawonn, Christian Braune, Matthias Steinbrecher, 2015, Texts in Computer Science.

Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data

Frank Klawonn, Christian Borgelt, Michael R. Berthold, 2010, Texts in Computer Science.

Widened Learning of Index Tracking Portfolios

Michael R. Berthold, Winfried Pohlmeier, Iuliia Gavriushina, 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).

M ONOTONIC D IFFERENTIABLE S ORTING N ETWORKS

C. Borgelt, Felix Petersen, Oliver Deussen, 2022 .

A conditional independence algorithm for learning undirected graphical models

Christian Borgelt, C. Borgelt, 2010, J. Comput. Syst. Sci..

Learning Undirected Possibilistic Networks with Conditional Independence Tests

Christian Borgelt, C. Borgelt, 2007, 2007 IEEE International Fuzzy Systems Conference.

On Identifying Tree-Structured Perfect Maps

Christian Borgelt, C. Borgelt, 2003, KI.

Learning from imprecise data: Possibilistic graphical models

Christian Borgelt, Rudolf Kruse, R. Kruse, 2002 .

Graphical Models: Representations for Learning, Reasoning and Data Mining

R. Kruse, C. Borgelt, M. Steinbrecher, 2009 .

Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains

Christian Borgelt, Sonja Grün, Denise Berger, 2009, Comput. Intell. Neurosci..

Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data

Christian Borgelt, Jörg Gebhardt, C. Borgelt, 2004 .

Agglomerative Fuzzy Clustering

Christian Borgelt, Rudolf Kruse, R. Kruse, 2016, SMPS.

Complexity distribution as a measure for assembly size and temporal precision

Christian Borgelt, Sonja Grün, Sebastien Louis, 2010, Neural Networks.

Behavioral Clustering for Point Processes

Christian Borgelt, Rudolf Kruse, Christian Braune, 2013, IDA.

Soft Pattern Mining in Neuroscience

Christian Borgelt, C. Borgelt, 2012, SMPS.

A Hierarchical Possibilistic Clustering

Heiko Timm, Christian Borgelt, James M. Keller, 2009 .

Learned Feature Generation for Molecules

Christian Borgelt, Michael R. Berthold, Patrick Winter, 2018, IDA.

Minimum Weight Triangulation by Cutting Out Triangles

Christian Borgelt, Christos Levcopoulos, Magdalene G. Borgelt, 2005, ISAAC.

Finding Explanations

F. Klawonn, F. Höppner, M. Berthold, 2020, Texts in Computer Science.

Learning Possibilistic Networks with a Global Evaluation Method

Christian Borgelt, C. Borgelt, 2007 .

Data Understanding

F. Klawonn, F. Höppner, M. Berthold, 2020, Texts in Computer Science.

Introduction to Neural Networks

C. Borgelt, Sanaz Mostaghim, M. Steinbrecher, 2016 .

Fixed Parameter Algorithms for the Minimum Weight Triangulation Problem

Christian Borgelt, Christos Levcopoulos, Magdalene G. Borgelt, 2008, Int. J. Comput. Geom. Appl..

Fuzzy Data Analysis

Christian Borgelt, Rudolf Kruse, Sanaz Mostaghim, 2016 .

Identification of neurons participating in cell assemblies

Christian Borgelt, Sonja Grün, Denise Berger, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Canonical Forms for Frequent Graph Mining

Christian Borgelt, C. Borgelt, 2006, GfKl.

A Decision Tree Plug-In for DataEngine

Christian Borgelt, C. Borgelt, 2004 .

Computational Intelligence: A Methodological Introduction

C. Borgelt, Sanaz Mostaghim, M. Steinbrecher, 2015 .

Residual Neural Networks to Distinguish Craving Smokers, Non-craving Smokers and Non-smokers by their EEG signals

Christian Borgelt, Sarah E. Donohue, Christoph Doell, 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

Accelerating fuzzy clustering

Christian Borgelt, C. Borgelt, 2009, Inf. Sci..

Multi-Layer Perceptrons

F. Klawonn, C. Borgelt, M. Steinbrecher, 2013 .

Computational Intelligence

C. Borgelt, Sanaz Mostaghim, M. Steinbrecher, 2018, Texts in Computer Science.

Fast construction of voxel-level functional connectivity graphs

Rudolf Kruse, Kristian Loewe, Christian Borgelt, 2014, BMC Neuroscience.

Computational Intelligence: A Methodological Introduction

R. Kruse, C. Borgelt, Sanaz Mostaghim, 2022, Texts in Computer Science.