Analysis of a Plurality Voting-based Combination of Classifiers

In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition result. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), m (number of classes), and p (probability that a single classifier is correct). Experimental results on the human face recognition problem show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates.     

A Weighted Voting Model of Associative Memory

This paper presents an analysis of a random access memory (RAM)-based associative memory which uses a weighted voting scheme for information retrieval. This weighted voting memory can operate in heteroassociative or autoassociative mode, can store both real-valued and binary-valued patterns and, unlike memory models, is equipped with a rejection mechanism. A theoretical analysis of the performance of the weighted voting memory is given for the case of binary and random memory sets. Performance measures are derived as a function of the model parameters: pattern size, window size, and number of patterns in the memory set. It is shown that the weighted voting model has large capacity and error correction. The results show that the weighted voting model can successfully achieve high-detection and -identification rates and, simultaneously, low-false-acceptance rates     

Generalizations of the Hamming Associative Memory

This Letter reviews four models of associative memory which generalize the operation of the Hamming associative memory: the grounded Hamming memory, the cellular Hamming memory, the decoupled Hamming memory, and the two-level decoupled Hamming memory. These memory models offer high performance and allow for a more practical hardware realization than the Hamming net and other fully interconnected neural net architectures.     

Recurrent neural nets as dynamical Boolean systems with applicationto associative memory

Discrete-time/discrete-state recurrent neural networks are analyzed from a dynamical Boolean systems point of view in order to devise new analytic and design methods for the class of both single and multilayer recurrent artificial neural networks. With the proposed dynamical Boolean systems analysis, we are able to formulate necessary and sufficient conditions for network stability which are more general than the well-known but restrictive conditions for the class of single layer networks: (1) symmetric weight matrix with (2) positive diagonal and (3) asynchronous update. In terms of design, we use a dynamical Boolean systems analysis to construct a high performance associative memory. With this Boolean memory, we can guarantee that all fundamental memories are stored, and also guarantee the size of the basin of attraction for each fundamental memory.     

Exact associative neural memory dynamics utilizing Boolean matrices

The exact dynamics of shallow loaded associative neural memories are generated and characterized. The Boolean matrix analysis approach is employed for the efficient generation of all possible state transition trajectories for parallel updated binary-state dynamic associative memories (DAMs). General expressions for the size of the basin of attraction of fundamental and oscillatory memories and the number of oscillatory and stable states are derived for discrete synchronous Hopfield DAMs loaded with one, two, or three even-dimensionality bipolar memory vectors having the same mutual Hamming distances between them. Spurious memories are shown to occur only if the number of stored patterns exceeds two in an even-dimensionality Hopfield memory. The effects of odd- versus even-dimensionality memory vectors on DAM dynamics and the effects of memory pattern encoding on DAM performance are tested. An extension of the Boolean matrix dynamics characterization technique to other, more complex DAMs is presented.     

A coupled gradient network approach for static and temporalmixed-integer optimization

Utilizes the ideas of artificial neural networks to propose new solution methods for a class of constrained mixed-integer optimization problems. These new solution methods are more suitable to parallel implementation than the usual sequential methods of mathematical programming. Another attractive feature of the proposed approach is that some global search mechanisms may be easily incorporated into the computation, producing results which are more globally optimal. To formulate the solution method proposed in this paper, a penalty function approach is used to define a coupled gradient-type network with an appropriate architecture, energy function and dynamics such that high-quality solutions may be obtained upon convergence of the dynamics. Finally, it is shown how the coupled gradient net may be extended to handle temporal mixed-integer optimization problems, and simulations are presented which demonstrate the effectiveness of the approach.     

An RCE-based Associative Memory with Application to Human Face Recognition

Many models of neural network-based associative memory have been proposed and studied. However, most of these models do not have a rejection mechanism and hence are not practical for many real-world associative memory problems. For example, in human face recognition, we are given a database of face images and the identity of each image. Given an input image, the task is to associate when appropriate the image with the corresponding name of the person in the database. However, the input image may be that of a stranger. In this case, the system should reject the input. In this paper, we propose a practical associative memory model that has a rejection mechanism. The structure of the model is based on the restricted Coulomb energy (RCE) network. The capacity of the proposed memory is desibed by two measures: the ability of the system to correctly identify known individuals, and the ability of the system to reject individuals who are not in the database. Experimental results are given which show how the performance of the system varies as the size of the database increases up to 1000 individuals.