Author name cluster

Nuno C. Marques

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

ECAI Conference 2010 Conference Paper

Implementing an Intelligent Moving Average with a Neural Network

Nuno C. Marques
Carlos Gomes

Recent results in hybrid neural networks using extended versions of the core method have shown that we can use background knowledge to guide back-propagation learning. This paper further explores this ideas by adding numeric functions to the encoded knowledge and using the traditional recursive Elman neural network model. An illustration of the properties of these neural networks will be used to calculate a simple moving average. Simulations on generated data and on the Eurostoxx50 financial index will illustrate the potential of such a strategy.

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ICAART Conference 2009 Conference Paper

A Batch Learning Vector Quantization Algorithm for Categorical Data

Ning Chen
Nuno C. Marques

Learning vector quantization (LVQ) is a supervised learning algorithm for data classification. Since LVQ is based on prototype vectors, it is a neural network approach particularly applicable in non-linear separation problems. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for mixed numerical and categorical data. Experiments on various data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to deal with categorical data.

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NeSy Conference 2008 Conference Paper

Guiding Backprop by Inserting Rules

Sebastian Bader 0001
Steffen Hölldobler
Nuno C. Marques

We report on an experiment where we inserted symbolic rules into a neural network during the training process. This was done to guide the learning and to help escape local minima. The rules are constructed by analysing the errors made by the network after training. This process can be repeated, which allows to improve the network performance again and again. We propose a general framework and provide a proof of concept of the usefullness of our approach.

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