Results 221 to 230 of about 2,247 (260)

Inference Machines for supervised Bluetooth localization

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
State space models, such as Kalman filters or Particle filters, have been applied to improve the accuracy of radio-wave-based localization. However, these models can drift radically when assumptions of the models are violated, and they do not have a mechanism to fix errors.
Tatsuya Ishihara, Kris M. Kitani, Chieko Asakawa, Michitaka Hirose   +3 more
openaire   +2 more sources

Relevance vector machine for UWB localization

2014 IEEE Wireless Communications and Networking Conference (WCNC), 2014
Wireless localization systems have a great importance in a variety of fields such as positioning and tracking systems. Specifically, in hash conditions, e.g., indoor environments, it is difficult to localize an agent with high accuracy due to radio blockage or insufficient information of anchors.
Thang Van Nguyen, Youngmin Jeong, Hyundong Shin   +2 more
openaire   +2 more sources

Are Gandy Machines Really Local?

2016
This paper discusses the empirical question concerning the physical realization (or implementation) of a computation. We give a precise definition of the realization of a Turing-computable algorithm into a physical situation. This definition is not based, as usual, on an interpretation function of physical states, but on an implementation function from
FANO, VINCENZO, GRAZIANI, PIERLUIGI, Roberto Macrelli, TAROZZI, GINO   +3 more
openaire   +2 more sources

Fuzzy localization in machine-to-machine networks

2017
La localisation est un processus de base dans les réseaux Machine-to-Machine (M2M). Cette thèse présente une nouvelle approche pour la localisation des noeuds mobiles dans les réseaux M2M sans fil. Notre approche est basée sur la conception d'un système de localisation floue adaptatif. La première contribution proposée est de considérer les éléments de
Mathlouthi Baccar, Noura, Bouallegue, Ridha   +1 more
openaire   +1 more source

Exploiting local structure in Boltzmann machines

Neurocomputing, 2011
Restricted Boltzmann machines (RBM) are well-studied generative models. For image data, however, standard RBMs are suboptimal, since they do not exploit the local nature of image statistics. We modify RBMs to focus on local structure by restricting visible-hidden interactions.
Hannes Schulz, Andreas C. Müller 0001, Sven Behnke   +2 more
openaire   +2 more sources

Machine Learning Based Localization Techniques

Advances in Artificial Intelligence and Machine Learning
Internet of Things (IoT) is growing at a pace that can be called ubiquitous. IoTs can be nominated as a subfield of a superset known as Wireless Sensor Networks (WSNs). These sensors are used for remote monitoring, target tracking, efficient transportation, industrial monitoring, patient observation through physiological sensors, smart agriculture ...
Aamir Zeb Shaikh, Shakil Ahmed, Kiran Hidayat, Minaal Ali, Abdul Rahim, Yamna Iqbal   +5 more
openaire   +2 more sources

Locality optimizations for parallel machines

1994
This paper focuses on the problem of locality optimizations for high-performance uniprocessor and multiprocessor systems. It shows that the problems of minimizing interprocessor communication and optimizing cache locality can be formulated in a similar manner.
openaire   +2 more sources

Localized Support Vector Machines for Classification

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
Support vector machines (SVMs) have been promising methods in pattern recognition because of their solid mathematical foundation. In this paper, we propose a localized SVM classification scheme (LSVM). In which we first cluster the training data in each category, and then train a set of SVMs based on these dusters. The SVMs trained from the clusters in
Ming Dong 0001, Jing Wu
openaire   +2 more sources

Locally linear support vector machines and other local models

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
The paper introduces various local models for solving machine learning (i.e., data mining) problems. In particular (and, due to their superior results) it focuses on a novel design of locally linear support vector machines classifiers. It presents them as powerful alternatives to the global (over the whole input space) nonlinear classifiers.
Vojislav Kecman, J. Paul Brooks
openaire   +1 more source

Fast and Scalable Local Kernel Machines.

J. Mach. Learn. Res., 2010
A computationally efficient approach to local learning with kernel methods is presented. The Fast Local Kernel Support VectorMachine (FaLK-SVM) trains a set of local SVMs on redundant neighbourhoods in the training set and an appropriate model for each query point is selected at testing time according to a proximity strategy.
Segata, Nicola, Blanzieri, Enrico
openaire   +2 more sources