Perceptron learning for reuse prediction

2016 ◽  
Author(s):  
Elvira Teran ◽  
Zhe Wang ◽  
Daniel A. Jimenez
Keyword(s):  
Perceptron Learning

Perceptron Learning in Optical Neural Computers

2020 ◽  
pp. 251-264
Author(s):  
David Brady ◽  
Demetri Psaltis
Keyword(s):  
Perceptron Learning

scholarly journals Implementation of a spike-based perceptron learning rule using TiO2−x memristors

2015 ◽  
Vol 9 ◽  
Author(s):  
Hesham Mostafa ◽  
Ali Khiat ◽  
Alexander Serb ◽  
Christian G. Mayr ◽  
Giacomo Indiveri ◽  
...  
Keyword(s):  
Learning Rule ◽  
Perceptron Learning

scholarly journals A "Thermal" Perceptron Learning Rule

1992 ◽  
Vol 4 (6) ◽  
pp. 946-957 ◽  
Author(s):  
Marcus Frean
Keyword(s):  
Gradient Descent ◽  
Learning Rule ◽  
Training Period ◽  
Simple Extension ◽  
Linear Threshold ◽  
Initial Setting ◽  
Separable Problems ◽  
Temperature Parameter ◽  
Perceptron Learning

The thermal perceptron is a simple extension to Rosenblatt's perceptron learning rule for training individual linear threshold units. It finds stable weights for nonseparable problems as well as separable ones. Experiments indicate that if a good initial setting for a temperature parameter, T0, has been found, then the thermal perceptron outperforms the Pocket algorithm and methods based on gradient descent. The learning rule stabilizes the weights (learns) over a fixed training period. For separable problems it finds separating weights much more quickly than the usual rules.

scholarly journals Using the Perceptron Algorithm to Find Consistent Hypotheses

1993 ◽  
Vol 2 (4) ◽  
pp. 385-387 ◽  
Author(s):  
Martin Anthony ◽  
John Shawe-Taylor
Keyword(s):  
Boolean Function ◽  
Simple Proof ◽  
Learning Algorithm ◽  
Perceptron Algorithm ◽  
Perceptron Learning ◽  
Perceptron Learning Algorithm

The perceptron learning algorithm quite naturally yields an algorithm for finding a linearly separable boolean function consistent with a sample of such a function. Using the idea of a specifying sample, we give a simple proof that, in general, this algorithm is not efficient.

Feature selection, perceptron learning, and a usability case study for text categorization

1997 ◽  
Vol 31 (SI) ◽  
pp. 67-73 ◽  
Author(s):  
Hwee Tou Ng ◽  
Wei Boon Goh ◽  
Kok Leong Low
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Perceptron Learning

TRAINING RECURRENT NEURAL NETWORKS — THE MINIMAL TRAJECTORY ALGORITHM

1992 ◽  
Vol 03 (01) ◽  
pp. 83-101 ◽  
Author(s):  
D. Saad
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Learning Rule ◽  
Weight Matrix ◽  
Training Methods ◽  
Input Output ◽  
Feed Forward ◽  
End Point ◽  
Common Weight ◽  
Perceptron Learning

The Minimal Trajectory (MINT) algorithm for training recurrent neural networks with a stable end point is based on an algorithmic search for the systems’ representations in the neighbourhood of the minimal trajectory connecting the input-output representations. The said representations appear to be the most probable set for solving the global perceptron problem related to the common weight matrix, connecting all representations of successive time steps in a recurrent discrete neural networks. The search for a proper set of system representations is aided by representation modification rules similar to those presented in our former paper,1 aimed to support contributing hidden and non-end-point representations while supressing non-contributing ones. Similar representation modification rules were used in other training methods for feed-forward networks,2–4 based on modification of the internal representations. A feed-forward version of the MINT algorithm will be presented in another paper.5 Once a proper set of system representations is chosen, the weight matrix is then modified accordingly, via the Perceptron Learning Rule (PLR) to obtain the proper input-output relation. Computer simulations carried out for the restricted cases of parity and teacher-net problems show rapid convergence of the algorithm in comparison with other existing algorithms, together with modest memory requirements.

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