Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition
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Publication:5957609
DOI10.1016/S0031-3203(00)00018-2zbMath0991.68071OpenAlexW2023935830MaRDI QIDQ5957609
Cheng-Lin Liu, Masaki Nakagawa
Publication date: 3 September 2002
Published in: Pattern Recognition (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0031-3203(00)00018-2
Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)
Related Items (8)
GEOMETRIC PROXIMITY GRAPHS FOR IMPROVING NEAREST NEIGHBOR METHODS IN INSTANCE-BASED LEARNING AND DATA MINING ⋮ A survey of deep network techniques all classifiers can adopt ⋮ Handwritten digit recognition: Benchmarking of state-of-the-art techniques. ⋮ Classifier combination based on confidence transformation ⋮ Regularized margin-based conditional log-likelihood loss for prototype learning ⋮ Neighborhood Property–Based Pattern Selection for Support Vector Machines ⋮ Initialization insensitive LVQ algorithm based on cost-function adaptation ⋮ A robust approach to text line grouping in online handwritten Japanese documents
Uses Software
Cites Work
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- An Optimal Global Nearest Neighbor Metric
- The optimal distance measure for nearest neighbor classification
- Discriminative learning for minimum error classification (pattern recognition)
- Finding Prototypes For Nearest Neighbor Classifiers
- A Branch and Bound Algorithm for Computing k-Nearest Neighbors
- Nearest neighbor pattern classification
- A Stochastic Approximation Method
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