Tony Van Gestel

发表

Identification of stable models in subspace identification by using regularization

Johan A. K. Suykens, Bart De Moor, Paul Van Dooren, 2001, IEEE Trans. Autom. Control..

Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002, Neural Processing Letters.

An empirical assessment of kernel type performance for least squares support vector machine classifiers

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

Bayesian Least Squares Support Vector Machines for Classification of Ovarian Tumors

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2004 .

Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, ICANN.

LS-SVMlab : a MATLAB / C toolbox for Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2007 .

Basic Methods of Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Financial time series prediction using least squares support vector machines within the evidence framework

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, IEEE Trans. Neural Networks.

Automatic relevance determination for Least Squares Support Vector Machines classifiers

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001, ESANN.

Large Scale Problems

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2003, AIME.

Classification of ovarian tumor using Bayesian least squares support vector machines

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2003 .

Bankruptcy prediction with least squares support vector machine classifiers

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

An overview and framework for PD backtesting and benchmarking

Bart Baesens, David Martens, Tony Van Gestel, 2010, J. Oper. Res. Soc..

Adding comprehensibility to support vector machine models using rule extraction techniques (poster)

Bart Baesens, Jan Vanthienen, David Martens, 2005 .

Volatility Tube Support Vector Machines

Johan A. K. Suykens, Bart De Moor, Tony Van Gestel, 2000 .

Knowledge discovery in a direct marketing case using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2001, Int. J. Intell. Syst..

Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000, PKDD.

Identification of positive real models in subspace identification by using regularization

Johan A. K. Suykens, Bart De Moor, Paul Van Dooren, 2003, IEEE Trans. Autom. Control..

Failure prediction with self organizing maps

Bart Baesens, Jan Vanthienen, Johan Huysmans, 2006, Expert Syst. Appl..

Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines

Bart Baesens, Kristiaan Pelckmans, Tony Van Gestel, 2005, NIPS 2005.

LS-SVM for Recurrent Networks and Control

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

A process model to develop an internal rating system: Sovereign credit ratings

Johan A. K. Suykens, Bart Baesens, Jan Vanthienen, 2006, Decis. Support Syst..

Benchmarking Least Squares Support Vector Machine Classifiers

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2004, Machine Learning.

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Kernel-based Classification for Financial Distress Detection Dirk Van Den Poel 4 Bayesian Kernel Based Classification for Financial Distress Detection

Johan A. K. Suykens, Bart Baesens, Dirk Van den Poel, 2022 .

Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002, Neural Computation.

From linear to non-linear kernel based classifiers for bankruptcy prediction

Bart Baesens, David Martens, Tony Van Gestel, 2010, Neurocomputing.

Least Squares Support Vector Machines

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state

Bart Baesens, David Martens, Tony Van Gestel, 2014, J. Oper. Res. Soc..

Bayesian interpretation of least squares support vector machines for financial time series prediction

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2001 .

From linear to Kernel Based Methods in Classification, Modelling and Prediction

Tony Van Gestel, 2002 .

Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

Sabine Van Huffel, Johan A. K. Suykens, Dirk Timmerman, 2003, Artif. Intell. Medicine.

Benchmarking state-of-the-art classification algorithms for credit scoring

Johan A. K. Suykens, Bart Baesens, Jan Vanthienen, 2003, J. Oper. Res. Soc..

Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines

Bart Baesens, Jan Vanthienen, David Martens, 2007, Eur. J. Oper. Res..

Least squares support vector machine classifiers: an empirical evaluation

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000 .

Decompositional Rule Extraction from Support Vector Machines by Active Learning

Bart Baesens, David Martens, Tony Van Gestel, 2009, IEEE Transactions on Knowledge and Data Engineering.

Country Corruption Analysis with Self Organizing Maps and Support Vector Machines

Bart Baesens, Jan Vanthienen, David Martens, 2006, WISI.

Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms

Bart Baesens, Jan Vanthienen, Christophe Mues, 2007, Eur. J. Oper. Res..

A support vector machine formulation to PCA analysis and its kernel version

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2003, IEEE Trans. Neural Networks.

Survey of specific electric consumptions and consumption profiles of domestic appliances

Lieven De Lathauwer, Bart De Moor, Tony Van Gestel, 2000 .

On Frequency Weighted Balanced Truncation: Hankel Singular Values and Error Bounds

Bart De Moor, Brian D. O. Anderson, Tony Van Gestel, 2001, Eur. J. Control.

Credit rating prediction using Ant Colony Optimization

Bart Baesens, Jan Vanthienen, David Martens, 2010, J. Oper. Res. Soc..

Neural network survival analysis for personal loan data

Bart Baesens, Jan Vanthienen, Dirk Van den Poel, 2005, J. Oper. Res. Soc..

Bayesian Inference for LS-SVM Models

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

LS-SVM for Unsupervised Learning

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2002 .

Special issue on intelligent information systems for financial engineering

Bart Baesens, Jan Vanthienen, Christophe Mues, 2006, Expert Syst. Appl..

Risk modelling and measurement

Bart Baesens, Tony Van Gestel, 2008 .

On frequency weighted balanced truncation: A constructive counterexample to Enns' conjecture

Bart De Moor, Brian D. O. Anderson, Tony Van Gestel, 2001, 2001 European Control Conference (ECC).

Credit Risk Management: Basic Concepts: Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital

Bart Baesens, Tony Van Gestel, B. Baesens, 2009 .

Forecasting and analyzing insurance companies' ratings

Bart Baesens, Jan Vanthienen, David Martens, 2007 .

Support Vector Machines : Least Squares Approaches and Extensions

Johan A. K. Suykens, Joos Vandewalle, Bart De Moor, 2003 .

Identifying positive real models in subspace identification by using regularization

Johan A. K. Suykens, Bart De Moor, Paul Van Dooren, 2003 .

Credit Risk Management

Bart Baesens, Tony Van Gestel, B. Baesens, 2008 .

A Bayesian nonlinear support vector machine error correction model

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2006 .