Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
A Constructive Incremental Learning Algorithm For Binary Classification Tasks, Christophe G. Giraud-Carrier, Tony R. Martinez
A Constructive Incremental Learning Algorithm For Binary Classification Tasks, Christophe G. Giraud-Carrier, Tony R. Martinez
Faculty Publications
This paper presents i-AA1*, a constructive, incremental learning algorithm for a special class of weightless, self-organizing networks. In i-AA1*, learning consists of adapting the nodes’ functions and the network’s overall topology as each new training pattern is presented. Provided the training data is consistent, computational complexity is low and prior factual knowledge may be used to “prime” the network and improve its predictive accuracy. Empirical generalization results on both toy problems and more realistic tasks demonstrate promise.
Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke
Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke
Faculty Publications
The paired-difference t-test is commonly used in the machine learning community to determine whether one learning algorithm is better than another on a given learning task. This paper suggests the use of the permutation test instead hecause it calculates the exact p-value instead of an estimate. The permutation test is also distribution free and the time complexity is trivial for the commonly used 10-fold cross-validation paired-difference test. Results of experiments on real-world problems suggest it is not uncommon to see the t-test estimate deviate up to 30-50% from the exact p-value.
Pair Attribute Learning: Network Construction Using Pair Features, Tony R. Martinez, Eric K. Henderson
Pair Attribute Learning: Network Construction Using Pair Features, Tony R. Martinez, Eric K. Henderson
Faculty Publications
We present the Pair Attribute Learning (PAL) algorithm for the selection of relevant inputs and network topology. Correlations on training instance pairs are used to drive network construction of a single-hidden layer MLP. Results on nine learning problems demonstrate 70% less complexity, on average, without a significant loss of accuracy.
Bias And The Probability Of Generalization, Tony R. Martinez, D. Randall Wilson
Bias And The Probability Of Generalization, Tony R. Martinez, D. Randall Wilson
Faculty Publications
In order to be useful, a learning algorithm must be able to generalize well when faced with inputs not previously presented to the system. A bias is necessary for any generalization, and as shown by several researchers in recent years, no bias can lead to strictly better generalization than any other when summed over all possible functions or applications. This paper provides examples to illustrate this fact, but also explains how a bias or learning algorithm can be “better” than another in practice when the probability of the occurrence of functions is taken into account. It shows how domain knowledge …