Paul Hollensen

Paul Hollensen
Dalhousie University | Dal · Faculty of Computer Science

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9
Publications
1,092
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61
Citations

Publications

Publications (9)
Article
Humans can point fairly accurately to memorized states when closing their eyes despite slow or even missing sensory feedback. It is also common that the arm dynamics changes during development or from injuries. We propose a biologically motivated implementation of an arm controller that includes an adaptive observer. Our implementation is based on...
Article
Biological systems are capable of learning that certain stimuli are valuable while ignoring the many that are not, and thus perform feature selection. In machine learning, one effective feature selection approach is the least absolute shrinkage and selection operator (LASSO) form of regularization, which is equivalent to assuming a Laplacian prior...
Article
Full-text available
Kohonen's self-organizing map (SOM) is used to map high-dimensional data into a low-dimensional representation (typically a 2-D or 3-D space) while preserving their topological characteristics. A major reason for its application is to be able to visualize data while preserving their relation in the high-dimensional input data space as much as possi...
Article
Full-text available
In this study we analyze a multilayer version of context-relevant topographical maps that we previously introduced. The hidden layers of this classifier are hierarchical two-dimensional topographical maps that differ from the conventional Self-Organizing Map in that their organizations are influenced by the context of the learning data. In this way...
Conference Paper
Full-text available
In this study we propose a hierarchical neural network that is able to generate a topographical map in its internal layer. The map significantly differs from the conventional Kohonen’s SOM, in that it preserves the topological characteristics in relevance to the context, for example the labels, of the data. This map is useful if we are interested i...
Article
Full-text available
While the target article provides a glowing account for the excitement in the field, we stress that hierarchical predictive learning in the brain requires sparseness of the representation. We also question the relation between Bayesian cognitive processes and hierarchical generative models as discussed by the target article.
Conference Paper
Full-text available
Advances in high frequency sonar have provided increasing resolution of sea bottom objects, providing higher fidelity sonar data for automated target recognition tools. Here we investigate if advanced techniques in the field of visual object recognition and machine learning can be applied to classify mine-like objects from such sonar data. In part...

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