Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
On Intelligence
Computing with active dendrites
Neurocomputing
How the brain might work: a hierarchical and temporal model for learning and recognition
How the brain might work: a hierarchical and temporal model for learning and recognition
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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This paper describes a biologically motivated approach, using Hierarchical Temporal Memory (HTM), to build a high-level self-organizing visual system for a soccer bot. Meanwhile it presents two unsupervised online learning algorithms for temporal patterns in HTMs. The algorithms were implemented in a simulated soccer bot for a real-world evaluation. After a training phase, the robot was able to recognize different static objects in the soccer field. It also learned and recognized high-level objects that are composed of simpler objects, with position invariance and was also able to learn and recognize motions in the objects, all in a completely unsupervised manner.