G. Horváth

发表

A roundoff error analysis of the Oja's subspace rule

Tamás Szabó, Gábor Horváth, T. Szabó, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

An Efficient Hardware Implementation of Feed-Forward Neural Networks

Tamás Szabó, Gábor Horváth, T. Szabó, 2001, IEA/AIE.

A full-parallel digital implementation for pre-trained NNs

G. Horvath, T. Szabo, L. Antoni, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A Robust LS-SVM Regression

Gábor Horváth, József Valyon, G. Horváth, 2007, IEC.

A WEIGHTED GENERALIZED LS-SVM

G. Horváth, J. Valyon, 2003 .

A generalised LS-SVM

G. Horváth, J. Valyon, 2003 .

CMAC : RECONSIDERING AN OLD NEURAL NETWORK

Gábor Horváth, G. Horváth, 2003 .

Cmac and Its Extensions for Efficient System Modelling

T. Szabó, G. Horváth, 1999 .

C. Micchelli, G. Horváth, K. Suykens, 2003 .

IMPROVED MODEL ORDER ESTIMATION FOR NONLINEAR DYNAMIC SYSTEMS

G. Horváth, L. Sragner, 2015 .

Extension of the nu-SVM range for classification

Bernhard Schölkopf, Jason Weston, Bernhard Schölkopf, 2003 .

Neural Networks in System Identification

Gábor HORVÁTH, G. Horváth, 2002 .

Neural networks from the perspective of measurement systems

G. Horvath, G. Horváth, 2003, Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412).

Optimal control with reinforcement learning using reservoir computing and Gaussian Mixture

I. Engedy, G. Horvath, G. Horváth, 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

Finite word length computational effects of the principal component analysis networks

Tamás Szabó, Gábor Horváth, T. Szabó, 1998, IEEE Trans. Instrum. Meas..

Improving the generalization capability of the binary CMAC

Tamás Szabó, Gábor Horváth, T. Szabó, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

TeleDAL: a regression-based template-less unsupervised method for finding anomalies in log sequences

G. Horváth, Péter Szilágyi, A. Mészáros, 2023, The Journal of Supercomputing.

A sparse least squares support vector machine classifier

G. Horvath, J. Valyon, G. Horváth, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

CMAC neural network as an SVM with B-spline kernel functions

G. Horvath, G. Horváth, 2003, Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412).

An Efficient Hardware Implementation of Feed-Forward Neural Networks

Tamás Szabó, Gábor Horváth, T. Szabó, 2001, Applied Intelligence.

Functional learning through kernels

G. Horváth, S. Basu, 2019 .

Microcontroller tracing in Hardware in the Loop tests integrating trace port measurement capability into NI VeriStand

Balazs Scherer, Gabor Horvath, G. Horváth, 2014, Proceedings of the 2014 15th International Carpathian Control Conference (ICCC).

Trace and debug port based watchdog processor

G. Horvath, B. Scherer, G. Horváth, 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

A WEIGHTED GENERALIZED LEAST – SQUARES SUPPORT VECTOR MACHINE

G. Horváth, J. Valyon, 2003 .