Manual fingerprint classification algorithms are very time consuming, and usually not accurate. Fast and accurate fingerprint classification is essential to each AFIS (Automatic Fingerprint Identification System). This paper investigates a fingerprint classification algorithm that reduces the complexity and costs associated with the fingerprint identification procedure. A new structural algorit...

We propose that the back propagation algorithm for supervised learning can be generalized, put on a satisfactory conceptual footing, and very likely made more efficient by defining the values of the output and input neurons as probabilities and varying the synaptic weights in the gradient direction of the log likelihood, rather than the 'error'. In the past thirty years many researchers have st...

We give a maximal independent set (MIS) algorithm that runs in $O(\log \log \Delta)$ rounds in the congested clique model, where $\Delta$ is the maximum degree of the input graph. This improves upon the $O(\frac{\log(\Delta) \cdot \log \log \Delta}{\sqrt{\log n}} + \log \log \Delta )$ rounds algorithm of [Ghaffari, PODC '17], where $n$ is the number of vertices of the input graph. In the first ...

This working paper proposes a new type of algorithm for the discovery of invariant, higher-order concepts in variable, noisy data. The algorithm is novel in that it is based on the unlimited addition of nodes and links to a recurrent connectionist network. Nodes are created to represent patterns of co-activation of existing nodes that are not sufficiently accounted for by existing nodes. This a...

It is well known that the variability in speech production due to task induced stress contributes signiicantly to loss in speech processing algorithm performance. If an algorithm could be formulated which detects the presence of stress in speech, then such knowledge could be used to monitor speaker state, improve the naturalness of speech coding algorithms, or increase the robustness of speech ...

Manual fingerprint classification algorithms are very time consuming, and usually not accurate. Fast and accurate fingerprint classification is essential to each AFIS (Automatic Fingerprint Identification System). This paper investigates a fingerprint classification algorithm that reduces the complexity and costs associated with the fingerprint identification procedure. A new structural algorit...

This paper examines issues relating to analog CMOS circuit implementation of the soft competitive neural learning algorithm. Simulations of a proposed implementation have been conducted based on hardware models constructed from actual measurements of 1.2 μ m CMOS analog components, primarily (nonlinear) Gilbert multipliers and associated circuits. We have used these same components in the past ...

The delta rule of associative learning has recently been used in several models of human category learning, and applied to categories with different relative frequencies, or base rates. Previous research has emphasized predictions of the delta rule after extensive learning. Our first experiment measures the relative acquisition rates of categories with different base rates, and the delta rule s...

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

In this paper, a new algorithm which is the result of the combination of cellular learning automata and frog leap algorithm (SFLA) is proposed for optimization in continuous, static environments.At the proposed algorithm, each memeplex of frogs is placed in a cell of cellular learning automata. Learning automata in each cell acts as the brain of memeplex, and will determine the strategy of moti...