180,443 research outputs found
Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites
This paper presents a spike-based model which employs neurons with
functionally distinct dendritic compartments for classifying high dimensional
binary patterns. The synaptic inputs arriving on each dendritic subunit are
nonlinearly processed before being linearly integrated at the soma, giving the
neuron a capacity to perform a large number of input-output mappings. The model
utilizes sparse synaptic connectivity; where each synapse takes a binary value.
The optimal connection pattern of a neuron is learned by using a simple
hardware-friendly, margin enhancing learning algorithm inspired by the
mechanism of structural plasticity in biological neurons. The learning
algorithm groups correlated synaptic inputs on the same dendritic branch. Since
the learning results in modified connection patterns, it can be incorporated
into current event-based neuromorphic systems with little overhead. This work
also presents a branch-specific spike-based version of this structural
plasticity rule. The proposed model is evaluated on benchmark binary
classification problems and its performance is compared against that achieved
using Support Vector Machine (SVM) and Extreme Learning Machine (ELM)
techniques. Our proposed method attains comparable performance while utilizing
10 to 50% less computational resources than the other reported techniques.Comment: Accepted for publication in Neural Computatio
The Strategic Exploitation of Limited Information and Opportunity in Networked Markets
This paper studies the effect of constraining interactions within a market. A model is analysed in which boundedly rational agents trade with and gather information from their neighbours within a trade network. It is demonstrated that a trader’s ability to profit and to identify the equilibrium price is positively correlated with its degree of connectivity within the market. Where traders differ in their number of potential trading partners, well-connected traders are found to benefit from aggressive trading behaviour.Where information propagation is constrained by the topology of the trade network, connectedness affects the nature of the strategies employed
Ensemble Learning for Free with Evolutionary Algorithms ?
Evolutionary Learning proceeds by evolving a population of classifiers, from
which it generally returns (with some notable exceptions) the single
best-of-run classifier as final result. In the meanwhile, Ensemble Learning,
one of the most efficient approaches in supervised Machine Learning for the
last decade, proceeds by building a population of diverse classifiers. Ensemble
Learning with Evolutionary Computation thus receives increasing attention. The
Evolutionary Ensemble Learning (EEL) approach presented in this paper features
two contributions. First, a new fitness function, inspired by co-evolution and
enforcing the classifier diversity, is presented. Further, a new selection
criterion based on the classification margin is proposed. This criterion is
used to extract the classifier ensemble from the final population only
(Off-line) or incrementally along evolution (On-line). Experiments on a set of
benchmark problems show that Off-line outperforms single-hypothesis
evolutionary learning and state-of-art Boosting and generates smaller
classifier ensembles
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