Open AccessBook
Knowledge-Based Intelligent Information and Engineering Systems
Reads0
Chats0
TLDR
Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition, and design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic 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 technology transfer [4]. A neural computation central to both the scientific and the technological analyses is the ART matching rule [5], which models the interaction between topdown expectation and bottom-up input, thereby creating a focus of attention which, in turn, determines the nature of coded memories. Sites of early and ongoing transfer of ART-based technologies include industrial venues such as the Boeing Corporation [6] and government venues such as MIT Lincoln Laboratory [7]. A recent report on industrial uses of neural networks [8] states: “[The] Boeing ... Neural Information Retrieval System is probably still the largest-scale manufacturing application of neural networks. It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters. Claimed savings in manufacturing costs are in millions of dollars per annum.” At Lincoln Lab, a team led by Waxman developed an image mining system which incorporates several models of vision and recognition developed in the Boston University Department of Cognitive and Neural Systems (BU/CNS). Over the years a dozen CNS graduates (Aguilar, Baloch, Baxter, Bomberger, Cunningham, Fay, Gove, Ivey, Mehanian, Ross, Rubin, Streilein) have contributed to this effort, which is now located at Alphatech, Inc. Customers for BU/CNS neural network technologies have attributed their selection of ART over alternative systems to the model's defining design principles. In listing the advantages of its THOT technology, for example, American Heuristics Corporation (AHC) cites several characteristic computational capabilities of this family of neural models, including fast on-line (one-pass) learning, “vigilant” detection of novel patterns, retention of rare patterns, improvement with experience, “weights [which] are understandable in real world terms,” and scalability (www.heuristics.com). Design principles derived from scientific analyses and design constraints imposed by targeted applications have jointly guided the development of many variants of the basic networks, including fuzzy ARTMAP [9], ART-EMAP [10], ARTMAP-IC [11],read more
Citations
More filters
Book ChapterDOI
PERMUTATION: A Corpus-Based Approach for Modeling Personality and Multimodal Expression of Affects in Virtual Characters
Céline Clavel,Jean-Claude Martin +1 more
TL;DR: The different approaches considered in personality psychology are revisited and it is shown that previous efforts to endow virtual agents with personality made only a limited use of these approaches.
Book ChapterDOI
Encouragement of collaborative learning based on dynamic groups
Ivan Srba,Mária Bieliková +1 more
TL;DR: The method is able to apply many types of students' characteristics as inputs, e.g. interests, knowledge, but also their collaborative characteristics, based on the Group Technology approach to support effective collaboration during learning.
Book ChapterDOI
A preliminary examination of background-color effects on the scores of computer-based english grammar tests using near-infrared spectroscopy
Atsuko K. Yamazaki,Kaoru Eto +1 more
TL;DR: The results suggest that white color may not be the best choice for a background color of a CBT, in terms of activating brain functions associated with linguistic tasks, even though a white background is commonly used for CBTs.
Book ChapterDOI
Natural language human-machine interface using artificial neural networks
Maciej Majewski,Wojciech Kacalak +1 more
TL;DR: A view is offered of the complexity of the recognition process of the operator's words and commands using neural networks made of a few layers of neurons.
Book ChapterDOI
A novel mobile learning assistant system
TL;DR: An assistant system can enable teachers to monitor the students' mobile phone screen synchronously thus know the learning state of the students, and it also supports real time interaction with the students by instant messages.
References
More filters
Journal ArticleDOI
Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
TL;DR: The fuzzy ARTMAP system is compared with Salzberg's NGE systems and with Simpson's FMMC system, and its performance in relation to benchmark backpropagation and generic algorithm systems.
Book ChapterDOI
Discovering Frequent Closed Itemsets for Association Rules
TL;DR: This paper proposes a new algorithm, called A-Close, using a closure mechanism to find frequent closed itemsets, and shows that this approach is very valuable for dense and/or correlated data that represent an important part of existing databases.
Journal ArticleDOI
ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network
TL;DR: A new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success, which is a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success.
Proceedings Article
Mining association rules with item constraints
TL;DR: In this paper, the problem of integrating constraints that are Boolean expressions over the presence or absence of items into the association discovery algorithm was considered and three integrated algorithms for mining association rules with item constraints were presented.