AIMC Topic: Artificial Intelligence

Clear Filters Showing 19641 to 19650 of 26332 articles

Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.

Neural networks : the official journal of the International Neural Network Society
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability of a model, which is able to take advantage of the availability of unlabeled samples. Moreover, this new bound improves state-of-the-art results even w...

A lane-level LBS system for vehicle network with high-precision BDS/GPS positioning.

Computational intelligence and neuroscience
In recent years, research on vehicle network location service has begun to focus on its intelligence and precision. The accuracy of space-time information has become a core factor for vehicle network systems in a mobile environment. However, difficul...

Automated classification of neurological disorders of gait using spatio-temporal gait parameters.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
OBJECTIVE: Automated pattern recognition systems have been used for accurate identification of neurological conditions as well as the evaluation of the treatment outcomes. This study aims to determine the accuracy of diagnoses of (oto-)neurological g...

Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not com...

A framework for final drive simultaneous failure diagnosis based on fuzzy entropy and sparse bayesian extreme learning machine.

Computational intelligence and neuroscience
This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction ...

A novel multiple instance learning method based on extreme learning machine.

Computational intelligence and neuroscience
Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the...

Potential application of machine learning in health outcomes research and some statistical cautions.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Traditional analytic methods are often ill-suited to the evolving world of health care big data characterized by massive volume, complexity, and velocity. In particular, methods are needed that can estimate models efficiently using very large dataset...

Validating estimates of prevalence of non-communicable diseases based on household surveys: the symptomatic diagnosis study.

BMC medicine
BACKGROUND: Easy-to-collect epidemiological information is critical for the more accurate estimation of the prevalence and burden of different non-communicable diseases around the world. Current measurement is restricted by limitations in existing me...

Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

Computational intelligence and neuroscience
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper p...

A novel tracking algorithm via feature points matching.

PloS one
Visual target tracking is a primary task in many computer vision applications and has been widely studied in recent years. Among all the tracking methods, the mean shift algorithm has attracted extraordinary interest and been well developed in the pa...