Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridgin...

Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...

Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance traditional classification algorithm (ELM). Although TELM has been widely researched and applied in field learning, need to solve two quadratic programming problems (QPPs) for greatly limited its development. In this paper, we propose novel framework called Lagrangian regularized twin (LRTELM). One si...

Extreme learning machines (ELMs) has been theoretically and experimentally proved to achieve promising performance at a fast speed for supervised classification tasks. However, it does not perform well on imbalanced binary tasks tends get biased toward the majority class. Besides, since large amount of training data with labels are always available in real world, there is an urgent demand devel...

Semi-supervised classification uses a large amount of unlabeled data to help a little amount of labeled data for designing classifiers, which has good potential and performance when the labeled data are difficult to obtain. This paper mainly discusses semi-supervised classification based on CPN (Counterpropagation Network). CPN and its revised models have merits such as simple structure, fast t...

Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method. It make use of the class membership of each data to be trained in the case of multiple classes, to improve the quality of classificat...

A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the label...

This dissertation studies the use of multiple classi ers (ensembles or committees) in learning tasks. Both theoretical and practical aspects of combining classi ers are studied. We consider two di erent goals: The rst is to achieve better classi cation rates. We analyze both the representation ability of ensembles and algorithms that search for a solution in this representation space. Second, w...

geological facies interpretation is essential for reservoir studying. the method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. use of neural networks as classifiers is increasing in different sciences like seismic. they are computer efficient and ideal for patterns identification. they can simply learn new algori...