SIAM Journal on Computing
The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Recognizing multistroke geometric shapes: an experimental evaluation
UIST '93 Proceedings of the 6th annual ACM symposium on User interface software and technology
The weighted majority algorithm
Information and Computation
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Experimental evaluation of an on-line scribble recognizer
Pattern Recognition Letters
Machine Learning
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16)
THE WEIGHTED MAJORITY ALGORITHM (Supersedes 89-16)
A model for image generation and symbol recognition through the deformation of lineal shapes
Pattern Recognition Letters
Parsing ink annotations on heterogeneous documents
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
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This paper describes a trainable recognizer for hand-drawn sketches using geometric features. We compare three different learning algorithms and select the best approach in terms of cost-performance ratio. The algorithms employ classic machine-learning techniques using a clustering approach. Experimental results show competing performance (95.1%) with the non-trainable recognizer (95.8%) previously developed, with obvious gains in flexibility and expandability. In addition, we study both their classification and learning performance with increasing number of examples per class.