Jonathan A. Tepper

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We present a neural network for real-time learning and mapping of patterns using an external performance indicator. In a non-stationary environment where new patterns are introduced over time, the learning process utilises a novel snap-drift algorithm that performs fast, convergent, minimalist learning (snap) when the overall network performance is poor and(More)
Connectionist parsers are neural-network-based systems (see Boxes 1 and 2) designed to process words or their syntactic types (tags) to produce a correct syntactic interpretation, or parse, of complete sentences. Parsers vary greatly in the way in which they tackle syntactic processing, and this is reflected in their modularity (or non-modularity) and in(More)
This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment(More)
This paper provides the most complete evidence to date on the importance of monetary aggregates as a policy tool in an inflation forecasting experiment. Every possible definition of 'money' in the USA is being considered for the full data period (1960 – 2006), in addition to two different approaches to constructing the benchmark asset, using the most(More)
This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map with laterally interconnected neurons. A derivation of functional-equivalence theory is used that allows the model to(More)
A neural network architecture is introduced for real-time learning of input sequences using external performance feedback. Some aspects of Adaptive Resonance Theory (ART) networks [1] are applied because they are able to function in a fast real-time adaptive active network environment where user requests and new proxylets (services) are constantly being(More)
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