A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Unifying Multiple Knowledge Domains Using the ARTMAP Information Fusion System

Sensors working at different times, locations, and scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels that are reconciled by their implicit underlying relationships. Even when such relationships are unknown to the user, an ARTMAP information fusion system discovers a hierarchical knowledge structure for a labeled dataset. The present paper addresses the problem of integrating two or more independent knowledge hierarchies based on the same low-level classes. The new system fuses independent domains into a unified knowledge structure, discovering cross-domain rules in this process. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, ARTMAP information fusion system features distributed code representations that exploit the neural network’s capacity for one-to-many learning. The fusion system software and testbed datasets are available from http://cns.bu.edu/techlabNational Science Foundation (SBE-0354378); National Geospatial-Intelligence Agency (NMA 201-01-1-2016

Distributed ARTMAP

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression. The model's computational learning rules correspond to paradoxical cortical data.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Designing Semantic Kernels as Implicit Superconcept Expansions

Recently, there has been an increased interest in the exploitation of background knowledge in the context of text mining tasks, especially text classification. At the same time, kernel-based learning algorithms like Support Vector Machines have become a dominant paradigm in the text mining community. Amongst other reasons, this is also due to their capability to achieve more accurate learning results by replacing standard linear kernel (bag-of-words) with customized kernel functions which incorporate additional apriori knowledge. In this paper we propose a new approach to the design of ‘semantic smoothing kernels’ by means of an implicit superconcept expansion using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations where (i) training data is scarce or (ii) the bag-ofwords representation is too sparse to build stable models when using the linear kernel