Performance characterisation in computer vision: statistics in testing and design
Imaging and vision systems
Classification with Synaptic Radial Basis Units
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Fundamenta Informaticae
On-line signature recognition based on VQ-DTW
Pattern Recognition
Computers in Biology and Medicine
An efficient face verification method in a transformed domain
Pattern Recognition Letters
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Multifeature knuckles parameterization
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
IEEE Transactions on Information Technology in Biomedicine
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
Auditory spectrum-based pitched instrument onset detection
IEEE Transactions on Audio, Speech, and Language Processing
Fast on-line signature recognition based on VQ with time modeling
Engineering Applications of Artificial Intelligence
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Is enough enough? what is sufficiency in biometric data?
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Model selection and assessment for classification using validation
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
International Journal of Speech Technology
Fundamenta Informaticae
Effect of small sample size on text categorization with support vector machines
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Hi-index | 0.14 |
We address the problem of determining what size test set guarantees statistically significant results in a character recognition task, as a function of the expected error rate. We provide a statistical analysis showing that if, for example, the expected character error rate is around 1 percent, then, with a test set of at least 10,000 statistically independent handwritten characters (which could be obtained by taking 100 characters from each of 100 different writers), we guarantee, with 95 percent confidence, that: (1) The expected value of the character error rate is not worse than 1.25 E, where E is the empirical character error rate of the best recognizer, calculated on the test set; and (2) a difference of 0.3 E between the error rates of two recognizers is significant. We developed this framework with character recognition applications in mind, but it applies as well to speech recognition and to other pattern recognition problems.