Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs
Neural Processing Letters
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Impartial trimmed k-means for functional data
Computational Statistics & Data Analysis
Functional support vector machines and generalized linear models for glacier geomorphology analysis
International Journal of Computer Mathematics - RECENT ADVANCES IN COMPUTATIONAL AND APPLIED MATHEMATICS IN SCIENCE AND ENGINEERING
On the use of the bootstrap for estimating functions with functional data
Computational Statistics & Data Analysis
Dual features functional support vector machines for fault detection of rechargeable batteries
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Representation of functional data in neural networks
Neurocomputing
Support vector machine for functional data classification
Neurocomputing
Strong universal consistency of neural network classifiers
IEEE Transactions on Information Theory
Automatic classification of granite tiles through colour and texture features
Expert Systems with Applications: An International Journal
Hi-index | 7.29 |
Automated classification of granite slabs is a key aspect of the automation of processes in the granite transformation sector. This classification task is currently performed manually on the basis of the subjective opinions of an expert in regard to texture and colour. We describe a classification method based on machine learning techniques fed with spectral information for the rock, supplied in the form of discrete values captured by a suitably parameterized spectrophotometer. The machine learning techniques applied in our research take a functional perspective, with the spectral function smoothed in accordance with the data supplied by the spectrophotometer. On the basis of the results obtained, it can be concluded that the proposed method is suitable for automatically classifying ornamental rock.