In this paper, we extend the Maximum uncertainty Linear Discriminant Analysis (MLDA), proposed recently for limited sample size problems, to its kernel version. The new Kernel Maximum uncertainty Discriminant Analysis (KMDA) is a two-stage method composed of Kernel Principal Component Analysis (KPCA) followed by the standard MLDA. In order to evaluate its effectiveness, experiments on face reco...

This paper presents an effective arrhythmia classification algorithm using the heart rate variability (HRV) signal. The proposed method is based on the Generalized Discriminant Analysis (GDA) feature reduction technique and the Multilayer Perceptron (MLP) neural network classifier. At first, nine linear and nonlinear features are extracted from the HRV signals and then these features are reduce...

In this paper, we present a hybrid graph-drawing algorithm (GDA) for layouting large, naturally-clustered, disconnected graphs. We called it a hybrid algorithm because it is an implementation of a series of already known graph-drawing and graphtheoretic procedures. We remedy in this hybrid the problematic nature of the current force-based GDA which has the inability to scale to large, naturally...

This paper critically evaluates and questions the primacy of accounting-based budget data in the decision-making process (usually at the strategic management level) within organisations. It proposes a theoretical synthesis of Peirce’s account of social inquiry and Habermas’ communicative action rationality to construct an epistemically robust, collectively oriented decision-making framework. Th...

This paper discusses how to automatically generate slide shows. The reported presentation system inputs documents annotated with the GDA tagset, an XML tagset which allows machines to automatically infer the semantic structure underlying the raw documents. The system picks up important topics in the input document on the basis of the semantic dependencies and coreferences identified from the ta...

Graphical User Interface (GUI) Driven Applications (GDAs) are ubiquitous. We present a model and techniques that take closed and monolithic GDAs and integrate them into an open, collaborative environment. The central idea is to objectify the GUI of a GDA, thereby creating an object that enables programmatic control of that GDA. We demonstrate a non-trivial application of these ideas by integrat...

Mixed-model assembly lines often create model imbalance due to differences in task times for the different product models. Smoothing algorithms guided by meta-heuristics that can escape local optimums can be used to reduce model imbalance. In this research we utilize the metaheuristics tabu search (TS), the great deluge algorithm (GDA) and record-to-record travel (RTR) to reduce three objective...

Averaging scheme has attracted extensive attention in deep learning as well traditional machine learning. It achieves theoretically optimal convergence and also improves the empirical model performance. However, there is still a lack of sufficient analysis for strongly convex optimization. Typically, about last iterate gradient descent methods, which referred to individual convergence, fails at...