As the number of smartphone users has grown recently, many context-aware services have been studied and launched. Activity recognition becomes one of the important issues for user adaptive services on the mobile phones. Even though many researchers have attempted to recognize a user's activities on a mobile device, it is still difficult to infer human activities from uncertain, incomplete and i...

PURPOSE To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained...

This paper describes the application of a hierarchical modular neural network for an advanced approach solving the knock detection task for combustion engines. The knock detection is realized on a two-level feature extraction approach. It is not only based on the popular cycle-by-cycle classification but a tendency index for the knock condition is determined. The experimental environment consis...

The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In t...

Transcription profiling experiments permit the expression levels of many genes to be measured simultaneously. Given profiling data from two types of samples, genes that most distinguish the samples (marker genes) are good candidates for subsequent in-depth experimental studies and developing decision support systems for diagnosis, prognosis, and monitoring. This work proposes a mixture of featu...

Although the performance of speech recognition systems has increased substantially over the last decades, there still remain a number of tasks which pose considerable problems for current state-of-the-art techniques. One of these tasks is the recognition of spontaneous speech which differs from read or planned speech in that its underlying dynamics change frequently over time. The negative effe...

This paper investigates a new method for improving the learning algorithm of Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS) and Conjugate Gradient (CG) as a second order optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield a much more efficient learning algorithm for ME structure. In addition, the experts and gating net...

The mixture-of-experts (ME) models can be useful to solve complicated classification problems in real world. However, in order to train the ME model with not only labeled data but also unlabeled data which are easier to come, a new learning algorithm that considers characteristics of the ME model is required. We proposed global-local co-training (GLCT), the hybrid training method of the ME mode...

Feed-forward control schemes require an inverse mapping of the controlled system. In adaptive systems this inverse mapping is learned from examples. The biological motor control is very redundant, as are many robotic systems, therefore the mapping is many-toone and the inverse problem is ill posed. In this paper we present a novel architecture and algorithms for the approximation and inversion ...