Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system c...

Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees—Hoeffding Trees with naive Bayes models at the leaf nodes—albeit with increased runtime compared to standard Hoeffding Trees. In this paper, we show that runtime can be reduced by replacing naive Bayes with perceptron c...

The HG literature has adopted so far the Perceptron reweighing rule because of its convergence guarantees. Yet, this rule is not suited to HG, as it fails at ensuring non-negativity of the weights. The first contribution of this paper is a solution to this impasse. I consider a variant of the Perceptron which truncates any update at zero, thus maintaining the weights non-negative in a principle...

Rosenblatt's perceptron is extended to (1) a multivalued perceptron and (2) to a continuous-valued perceptron. It shown that any function that can be represented by the multivalued perceptron can be learned in a nite number of steps, and any function that can represented by the continuous perceptron can be learned with arbitrary accuracy in a nite number of steps. The whole apparatus is deened ...

The classical perceptron algorithm is an elementary row-action/relaxation algorithm for solving a homogeneous linear inequality system Ax > 0. A natural condition measure associated with this algorithm is the Euclidean width τ of the cone of feasible solutions, and the iteration complexity of the perceptron algorithm is bounded by 1/τ, see Rosenblatt 1962 [20]. Dunagan and Vempala [5] have deve...

Recently, Peña and Sohèili presented a deterministic rescaling perceptron algorithm and proved that it solves a feasible perceptron problem in O(mn log(ρ−1)) perceptron update steps, where ρ is the radius of the largest inscribed ball. The original stochastic rescaling perceptron algorithm of Dunagan and Vempala is based on systematic increase of ρ, while the proof of Peña and Sohèili is based ...

In this paper, both the architecture and learning procedure underlying the parallel layer perceptron is presented. This topology, di1erent to the previous ones, uses parallel layers of perceptrons to map nonlinear input–output relationships. Comparisons between the parallel layer perceptron, multi-layer perceptron and ANFIS are included and show the e1ectiveness of the proposed topology. c © 20...

An implementation of an optical perceptron with a soft optical threshold trained with an adapted BP algorithm is described as a precursor to an optical multilayer perceptron (MLP). It has 64 inputs and ten outputs. The soft threshold is implemented by a liquid crystal light valve. Experimental results on perceptron recall are also reported. The eeect of a modiied grey-scale to weight mapping fo...