Image enhancement and thresholding by optimization of fuzzy compactness
Pattern Recognition Letters
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural network, self-organization and object extraction
Pattern Recognition Letters - Special issue on artificial neural networks
On the exponential value of labeled samples
Pattern Recognition Letters
Face recognition using a hybrid supervised/unsupervised neural network
Pattern Recognition Letters
Colour image segmentation by modular neural network
Pattern Recognition Letters
Time-series segmentation using predictive modular neural networks
Neural Computation
Incorporating test inputs into learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Color and spatial feature for content-based image retrieval
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Partially supervised clustering for image segmentation
Pattern Recognition
Semi-supervised learning in knowledge discovery
Fuzzy Sets and Systems
| Hi-index | 0.00 |
This chapter presents an approach of using unlabeled data for learning classification problems. The chapter consists of two parts. In the first part of the chapter, an approach of using both labeled and unlabeled data to train a multilayer percetron is presented. The approach banks on the assumption that regions of low pattern density usually separate data classes. The unlabeled data are iteratively preprocessed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved by using the approach when the labeled data do not adequately represent the entire class distributions. In the second part of the chapter, we propose a quality function for learning decision boundary between data clusters from unlabeled data. The function is based on third order polynomials. The objective of the quality function is to find a place in the input sparse in data points. By maximizing the quality function, we find a decision boundary between data clusters. A superiority of the proposed quality function over the other similar functions as well as the conventional clustering algorithms tested has been observed in the experiments.