ARTMAP

ARTMAP combines two slightly modified ART-1 or ART-2 units into a supervised learning structure.


Articles & Tech Transfers


Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion
Abstract The aim of this paper is to outline a multiple scale neural model to recognise colour images of textured scenes. This model combines colour and textural information in order to recognise colour texture images through the ...

AG-ART: An adaptive approach to evolving ART architectures
Abstract This paper focuses on classification problems, and in particular on the evolution of ARTMAP architectures using genetic algorithms, with the objective of improving generalization performance and alleviating the adaptive ...

Coordinated machine learning and decision support for situation awareness
Abstract Domains such as force protection require an effective decision maker to maintain a high level of situation awareness. A system that combines humans with neural networks is a desirable approach. Furthermore, it is ...

Biased ART: A neural architecture that shifts attention toward previously disregarded features following an incorrect prediction
Abstract Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved ...

Self-supervised ARTMAP
Abstract Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input ...

Soft-clustering and improved stability for adaptive resonance theory neural networks
Abstract Stability and plasticity in learning systems are both equally essential, but achieving stability and plasticity simultaneously is difficult. Adaptive resonance theory (ART) neural networks are known for their plastic and ...

On the match tracking anomaly of the ARTMAP neural network
Abstract This article analyses the match tracking anomaly (MTA) of the ARTMAP neural network. The anomaly arises when an input pattern exactly matches its category prototype that the network has previously learned, and the network ...

A constructive, incremental-learning network for mixture modeling and classification
Abstract Gaussian ARTMAP (GAM) is a supervised-learning adaptive resonance theory (ART) network that uses gaussian-defined receptive fields. Like other ART networks, GAM incrementally learns and constructs a representation of ...

Enhanced exchange heuristic based resource constrained scheduler using ARTMAP
Abstract The Exchange Heuristic (EH) has demonstrated superior results compared with other RCS methods in solving Resource Constrained Scheduling (RCS) problems. Selecting the most promising target constitutes the success of EH. The ...

Polytope ARTMAP: Pattern classification without vigilance based on general geometry categories
Abstract This paper proposes polytope ARTMAP (PTAM), an adaptive resonance theory (ART) network for classification tasks which does not use the vigilance parameter.. This feature is due to the geometry of categories in PTAM, which ...

A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function
Abstract In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized ...

Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data
Abstract To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has ...

ART-MMAP: A neural network approach to subpixel classification
Abstract Global or continental-scale land cover mapping with remote sensing data is limited by the spatial characteristics of satellites. Subpixel-level mapping is essential for the successful description of many land cover patterns ...

On the design of intelligent robotic agents for assembly
Abstract Robotic agents can greatly be benefited from the integration of perceptual learning in order to monitor and adapt to changing environments. To be effective in complex unstructured environments, robots have to perceive the ...

Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network
Abstract This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural ...

Multiclass cancer classification using semisupervised ellipsoid ARTMAP and particle swarm optimization with gene expression data
Abstract It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. With the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for ...

New cancer diagnosis modeling using boosting and projective adaptive resonance theory with improved reliable index
Abstract An optimal and individualized treatment protocol based on accurate diagnosis is urgently required for the adequate treatment of patients. For this purpose, it is important to develop a sophisticated algorithm that can manage ...

Adaptive resonance theory based neural networks - the ‘ART’ of real-time pattern recognition in chemical process monitoring?
Abstract The family of artificial neural networks based on Adaptive Resonance Theory (ART) forms a collection of distinct mathematical pattern recognition methods. The classification of sensor signals, process data analysis, spectral ...

MicroARTMAP for pattern recognition problems
Abstract Pattern recognition is an important aspect of a dominant technology such as machine intelligence. Domain specific fuzzy-neuro models particularly for the ?black box? implementation of pattern recognition applications have ...

Supervised training of a neural network
Abstract The present invention provides a system and method for supervised training of a neural network. A neural network architecture and training method is disclosed that is a modification of an ARTMAP architecture. The modified ...

Determination of concentrations of biological substances using raman spectroscopy and artificial neural networks discriminator
Abstract The concentration of a substance, such as glucose, in a biological sample, such as human tissue (e.g. the skin of an index finger) is non-invasively determined by directing the output beam of a laser diode onto and into the ...

Machine fault diagnostics system and method
Abstract The invention provides a machine fault diagnostic system to help ensure effective equipment maintenance. The major technique used for fault diagnostics is a fault diagnostic network (FDN) which is based on a modified ARTMAP ...

Hypersphere ART and ARTMAP for Unsupervised and Supervised, Incremental Learning
Abstract A novel adaptive resonance theory (ART) neural network architecture is being proposed. The new model, called Hypersphere ART (H-ART) is based on the same principals like Fuzzy-ART does and, thus, inherits most of its ...

Category regions as new geometrical concepts in Fuzzy-ART and Fuzzy-ARTMAP
Abstract In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy- ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of ...

Machine vision approach for robotic assembly.
Abstract Purpose ? Outcome with a novel methodology for online recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. Design/methodology/approach ? The ...

Adaptive resonance associative map
Abstract This article introduces a neural architecture termed Adaptive Resonance Associative Map (ARAM) that extends unsupervised Adaptive Resonance Theory (ART) systems for rapid, yet stable, heteroassociative learning. ARAM can be ...

A what-and-where fusion neural network for recognition and tracking of multiple radar emitters
Abstract A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered with position-specific information from ...

Distributed prediction and hierarchical knowledge discovery by ARTMAP neural networks
Abstract Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for ...

Threshold determination for ARTMAP-FD familiarity discrimination
Abstract The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes ...

ART and ARTMAP neural networks for applications: Self-organizing learning, recognition, and prediction
Abstract ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing ...

Distributed activation, search, and learning by ART and ARTMAP neural networks
Abstract A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning ...

A neural network architecture for autonomous learning, recognition, and prediction in a nonstationary world
Abstract In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and name novel objects without ...

Rule extraction: From neural architecture to symbolic representation
Abstract This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called furry ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network ...

A distributed outstar network for spatial pattern learning
Abstract The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals for a source node cause weights to learn and recall arbitrary patterns across a ...

Comparison of regression modeling and neural network modeling for predicting postoperative adverse events
Abstract Developing clinical models to predict adverse events and mortality following major clinical interventions may be an important part of a quality assessment program. Neural Networks (NN) offer certain advantages when compared ...

Integrating symbolic and neural processing in a self-organizing architecture for pattern recognition and prediction
Abstract The apparent dichotomy between symbolic AI processing and distributed neural processing cannot be absolute, since neural networks that capture essential features of human intelligence will also model some of the symbolic ...

Fusion ART An adaptive fuzzy network for multi channel classification
Abstract Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The ...

Attentive supervised learning and recognition by an adaptive resonance system
Abstract ARTMAP is a class of neural network architectures that employ attentional mechanisms to perform incremental supervised learning of recognition categories and multidimensional maps. The first ARTMAP system (Carpenter, ...

A neural network architecture for fast on line supervised learning and pattern recognition
Abstract This chapter describes a new neural network architecture, called ARTMAP (Carpenter, Grossberg, and Reynolds, 1991), that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors in recognition categories ...

A self organizing ARTMAP neural architecture for supervised learning and pattern recognition
Abstract This paper announces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors in recognition categories based on predictive success. This ...

Default ARTMAP 2
Abstract Default ARTMAP combines winner-take-all category node activation during training, distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network system ...

Unifying multiple knowledge domains using the ARTMAP information fusion system
Abstract 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 ...

ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network
Abstract This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This ...

ARTMAP-FTR: A neural network for fusion target recognition, with application to sonar classification
Abstract ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool ...

ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases
Abstract For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking ...

Neural sensor fusion for spatial visualization on a mobile robot
Abstract An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots ...

Distributed ARTMAP: a neural network for fast distributed supervised learning
Abstract 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 ...

ART neural networks for remote sensing image analysis
Abstract ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing ...

Distributed ARTMAP
Abstract 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 ...

A Neural Network Method for Mixture Estimation for Vegetation Mapping
Abstract While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife ...

A neural network method for efficient vegetation mapping
Abstract This article describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra ...

Neural-network models of learning and memory: leading questions and an emerging framework
Abstract Real-time neural-network models provide a conceptual framework for formulating questions about the nature of cognition, an architectural framework for mapping cognitive functions to brain regions, a semantic framework for ...

A neural network method for land use change classification, with application to the Nile River delta
Abstract Detecting and monitoring changes in conditions at the Earth?s surface are essential for understanding human impact on the environment and for assessing the sustainability of development. In the next decade, NASA will gather ...

ARTMAP neural network classification of land use change
Abstract The ability to detect and monitor changes in land use is essential for assessment of the sustainability of development. In the next decade, NASA will gather high-resolution multi-spectral and multi-temporal data, which could ...

ARTMAP neural networks for information fusion and data mining: Map production and target recognition
Abstract The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial ...

Adaptive Resonance Theory
Abstract Principles derived from an analysis of experimental literatures in vision, speech, cortical development, and reinforcement learning, including attentional blocking and cognitive-emotional interactions, led to the ...

Self-Organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks
Abstract Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and ...

Self-organizing hierarchical knowledge discovery by an ARTMAP information fusion system
Abstract Classifying terrain or objects may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from users with different goals and situations. Current fusion methods ...


Software


Biased ARTMAP
Description Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved ...

Self-Supervised ARTMAP
Description Self-Supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled ...


Datasets


Boston Remote Sensing Testbed (preprocessed MAT files)
Description

These MATLAB-ready .mat data files contain pixel data for the Boston remote sensing testbed. This is the same data that is provided with Classer. However, all the Classer preprocessing scripts have been run, so the mat file

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Circle in Square Benchmark
Description This is the circle in the square benchmark, consisting of three data sets: a 100-dimension text file set, a 1000-dimension text file set, and a Matlab set. This benchmark is extensively used in the publication: Carpenter, ...