Vector quantization and signal compression
Vector quantization and signal compression
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Digital Image Processing
Generalized Projections: A Tool for Cursive Handwriting Normalization
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
On Intelligence
A SVM-based cursive character recognizer
Pattern Recognition
A computational model of the cerebral cortex
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Robust character recognition using a hierarchical bayesian network
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
A MDRNN-SVM hybrid model for cursive offline handwriting recognition
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Feature representation selection based on Classifier Projection Space and Oracle analysis
Expert Systems with Applications: An International Journal
Evolving hierarchical temporal memory-based trading models
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive handwriting recognition problems and compare it to a current state-of-the-art support vector machine implementation. We also examine whether two pre-processing techniques can enhance the temporal pooling algorithm's performance. Our results show that a relatively simple temporal pooling approach can produce recognition rates that approach the current state-of-the-art without the need for extensive tuning of parameters. We also show that temporal pooling performance is surprisingly unaffected by the use of preprocessing techniques.