Norbert Jankowski

发表

Fast heterogeneous boosting

Norbert Jankowski, N. Jankowski, 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).

Meta-learning and new ways in model construction for classification problems

Norbert Jankowski, 2013 .

Optimal transfer function neural networks

Wlodzislaw Duch, Norbert Jankowski, 2001, ESANN.

Approximation and classification with RBF-type Neural Networks using flexible local and semi-local transfer functions1

Norbert Jankowski, 1999 .

Heterogenous committees with competence analysis

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

Initialization of adaptive parameters in density networks

Norbert Jankowski, N. Jankowski, 2000 .

Handwritten Digit, Recognition Road to Contest victory

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

O (m log m) instance selection algorithms—RR-DROPs

Norbert Jankowski, Marek Orliński, N. Jankowski, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Survey of Neural Transfer Functions

Norbert Jankowski, Wlodzislaw Duch, N. Jankowski, 1999 .

Learning Machines

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2006, Feature Extraction.

Handwri'tten Digit, Recogn'it'ion RoadtoC"ontest victory

Norbert Jankowski, 2007 .

Comparison of Instance Selection Algorithms II. Results and Comments

Marek Grochowski, Norbert Jankowski, N. Jankowski, 2004, ICAISC.

A Fast Neural Network Learning Algorithm with Approximate Singular Value Decomposition

Norbert Jankowski, Rafał Linowiecki, N. Jankowski, 2019, Int. J. Appl. Math. Comput. Sci..

Meta-Learning in Computational Intelligence

Norbert Jankowski, Włodzisław Duch, Krzysztof Grabczewski, 2013, Meta-Learning in Computational Intelligence.

Neural methods of knowledge extraction

Norbert Jankowski, Krzysztof Grabczewski, Wøodzisøaw Duch, 2000 .

Make it cheap: Learning with O(nd) complexity

Wlodzislaw Duch, Norbert Jankowski, Tomasz Maszczyk, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Meta-learning with Machine Generators and Complexity Controlled Exploration

Norbert Jankowski, Krzysztof Grabczewski, Krzysztof Grabczewski, 2006, ICAISC.

Learning Machines Information Distribution System with Example Applications

Norbert Jankowski, Krzysztof Grabczewski, 2008, Computer Recognition Systems 2.

Statistical Control of RBF-like Networks for Classification

Visakan Kadirkamanathan, Norbert Jankowski, V. Kadirkamanathan, 1997, ICANN.

Building meta-learning algorithms basing on search controlled by machine complexity

Norbert Jankowski, Krzysztof Grabczewski, Krzysztof Grabczewski, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Transfer functions: hidden possibilities for better neural networks

Wlodzislaw Duch, Norbert Jankowski, Wlodzislaw Duch, 2001, ESANN.

Fast t-SNE algorithm with forest of balanced LSH trees and hybrid computation of repulsive forces

Norbert Jankowski, Marek Orlinski, Marek Orlinski, 2020, Knowl. Based Syst..

Fast Algorithm for Prototypes Selection - Trust-Margin Prototypes

Norbert Jankowski, Marek Orlinski, N. Jankowski, 2019, ICAISC.

Versatile and Efficient Meta-Learning Architecture: Knowledge Representation and Management in Computational Intelligence

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

Mining for Complex Models Comprising Feature Selection and Classification

Norbert Jankowski, Krzysztof Grabczewski, 2006, Feature Extraction.

Comparison of Instances Seletion Algorithms I. Algorithms Survey

Marek Grochowski, Norbert Jankowski, N. Jankowski, 2004, ICAISC.

Optimization and Interpretation of Rule-based Classifiers

Wlodzislaw Duch, Norbert Jankowski, Krzysztof Grabczewski, 2000, Intelligent Information Systems.

STATISTICAL CONTROL OF GROWING AND PRUNING IN RBF-LIKE NEURAL NETWORKS

Visakan Kadirkamanathan, Norbert Jankowski, V. Kadirkamanathan, 1997 .

Discrete Quasi-gradient Features Weighting Algorithm

Norbert Jankowski, 2003 .

Instances Selection Algorithms in the Conjunction with LVQ

Marek Grochowski, Norbert Jankowski, N. Jankowski, 2005, Artificial Intelligence and Applications.

Universal Meta-Learning Architecture and Algorithms

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2011, Meta-Learning in Computational Intelligence.

Task Management in Advanced Computational Intelligence System

Norbert Jankowski, Krzysztof Grabczewski, 2010, ICAISC.

Graph-based generation of a meta-learning search space

Norbert Jankowski, N. Jankowski, 2012, Int. J. Appl. Math. Comput. Sci..

Initialization and optimization of multilayered perceptrons

Norbert Jankowski, Wøodzisøaw Duch, Rafao Adamczak, 1997 .

Increasing Efficiency of Data Mining Systems by Machine Unification and Double Machine Cache

Norbert Jankowski, Krzysztof Grabczewski, 2010, ICAISC.

New developments in the Feature Space Mapping model

Norbert Jankowski, Rafal Adamczak, N. Jankowski, 2000 .

Control of complex machines for meta-learning in computational intelligence

Norbert Jankowski, Krzysztof Grabczewski, 2007 .

Approximation and Classification in Medicine with IncNet Neural Networks1

Norbert Jankowski, 1999 .

GAINEDKNOWLEDGE EXCHANGE AND ANALYSISFOR META-LEARNING

Norbert Jankowski, Krzysztof Grabczewski, 2007 .

Taxonomy of neural transfer functions

Wlodzislaw Duch, Norbert Jankowski, Wlodzislaw Duch, 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.

NEW NEURAL TRANSFER FUNCTIONS

Norbert Jankowski, N. Jankowski, 2007 .

Flexible Transfer Functions with Ontogenic Neural Networks

Norbert Jankowski, N. Jankowski, 1999 .

Prototype-Based Kernels for Extreme Learning Machines and Radial Basis Function Networks

Norbert Jankowski, 2018, ICAISC.

Analysis of Feature Weighting Methods Based on Feature Ranking Methods for Classification

Norbert Jankowski, Krzysztof Usowicz, 2011, ICONIP.

Chapter 1 Learning Machines

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2012 .

Saving time and memory in computational intelligence system with machine unification and task spooling

Norbert Jankowski, Krzysztof Grabczewski, N. Jankowski, 2011, Knowl. Based Syst..