Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Rough mereological foundations for design, analysis, synthesis, and control in distributed systems
Information Sciences: an International Journal - From rough sets to soft computing
Constructing rough mereological granules of classifying rules and classifying algorithms
Technologies for constructing intelligent systems
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Rough set approach to domain knowledge approximation
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Rough sets in perception-based computing
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Outlier Detection: An Approximate Reasoning Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Interactive Granular Computing in Rightly Judging Systems
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
A wistech paradigm for intelligent systems
Transactions on rough sets VI
Discovery of process models from data and domain knowledge: a rough-granular approach
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Discovery of processes and their interactions from data and domain knowledge
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Transactions on Rough Sets V
| Hi-index | 0.00 |
Pattern recognition methods for complex structured objects such as handwritten characters often have to deal with vast search spaces. Developed techniques, despite significant advancement in the last decade, still face some performance barriers. We believe that additional knowledge about the structure of patterns, elicited from humans perceptions, will help improve the recognition’s performance, especially when it comes to classify irregular, outlier cases. We propose a framework for the transfer of such knowledge from human experts and show how to incorporate it into the learning process of a recognition system using methods based on rough mereology. We also demonstrate how this knowledge acquisition can be conducted in an interactive manner, with a large dataset of handwritten digits as an example.