scholarly journals Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization

2019 ◽  
pp. 280-288 ◽  
Author(s):  
Huang Fang ◽  
Minhao Cheng ◽  
Cho-Jui Hsieh ◽  
Michael Friedlander
Keyword(s):  
Large Scale ◽  
Linear Classifiers ◽  
Fast Training

scholarly journals Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiancheng Liu ◽  
Congxiang Tian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rapid Development ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Fast Training ◽  
Simulation Calculation ◽  
Monitoring Platform

With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.

scholarly journals A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiusheng Chen ◽  
Xiaoyu Zhang ◽  
Kai Guo
Keyword(s):  
Large Scale ◽  
Novelty Detection ◽  
Imbalanced Data ◽  
Feature Space ◽  
Training Time ◽  
Angular Region ◽  
The Core ◽  
Large Scale Problems ◽  
Fast Training ◽  
Core Set

A large vector-angular region and margin (LARM) approach is presented for novelty detection based on imbalanced data. The key idea is to construct the largest vector-angular region in the feature space to separate normal training patterns; meanwhile, maximize the vector-angular margin between the surface of this optimal vector-angular region and abnormal training patterns. In order to improve the generalization performance of LARM, the vector-angular distribution is optimized by maximizing the vector-angular mean and minimizing the vector-angular variance, which separates the normal and abnormal examples well. However, the inherent computation of quadratic programming (QP) solver takesO(n3)training time and at leastO(n2)space, which might be computational prohibitive for large scale problems. By(1+ε)  and  (1-ε)-approximation algorithm, the core set based LARM algorithm is proposed for fast training LARM problem. Experimental results based on imbalanced datasets have validated the favorable efficiency of the proposed approach in novelty detection.

scholarly journals SUPPORTING THE MANAGEMENT OF HUMANITARIAN OPERATIONS CONCERNING MIGRATION MOVEMENTS WITH REMOTE SENSING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 233-240
Author(s):  
L. Wickert ◽  
M. Bogen ◽  
M. Richter
Keyword(s):  
Large Scale ◽  
Training Data ◽  
Sufficient Information ◽  
Refugee Camps ◽  
Humanitarian Crisis ◽  
Data Annotation ◽  
Machine Learning Methods ◽  
Humanitarian Operations ◽  
Fast Training

Abstract. The various forms of humanitarian operations include operations concerning the management of migrant movements and refugees. Managing those operations is non-trivial. A large number of refugees have to be welcomed, registered, forwarded, and be given supplies and accommodation. This is due to a lack of current and sufficient information about the refugees, making planning and execution of operations challenging, expensive and cumbersome. The earlier information about the refugees is available, the better. The method “Dwelling Detection”, conducted on satellite imagery of refugee camps, can provide large-scale heads-up information fast, complementing information already available to operators at the ground. With “Dwelling Detection”, dwellings in a camp and their extent are detected using machine learning methods. An estimate of inhabitants of the camp is computed using the number and the extent of the detected dwellings. Our workflow uses a Faster R-CNN, an object detection network. To train the network, we developed a fast training data annotation workflow. We use the dwellings detected by the faster R-CNN to estimate a number of inhabitants. The quality of the analysis can be evaluated by a confidence-metric, computed out of the results of the Faster R-CNN. The results can be used in humanitarian operations. We tested the workflow using different configurations and data. From those tests, we give recommendations on how to build a dwelling detection classifier. We propose to humanitarian operators to build a dwelling detection classifier according to our recommendations and use satellite images in actual humanitarian operations. This could help to reduce stress for all people involved in a humanitarian (crisis) situation.

scholarly journals Fast Training Logistic Regression via Adaptive Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yunsheng Song ◽  
Xiaohan Kong ◽  
Shuoping Huang ◽  
Chao Zhang
Keyword(s):  
Logistic Regression ◽  
Random Sampling ◽  
Adaptive Sampling ◽  
Large Scale ◽  
Likelihood Function ◽  
Classification Performance ◽  
Training Data ◽  
Gradient Estimation ◽  
Training Time ◽  
Fast Training

Logistic regression has been widely used in artificial intelligence and machine learning due to its deep theoretical basis and good practical performance. Its training process aims to solve a large-scale optimization problem characterized by a likelihood function, where the gradient descent approach is the most commonly used. However, when the data size is large, it is very time-consuming because it computes the gradient using all the training data in every iteration. Though this difficulty can be solved by random sampling, the appropriate sampled examples size is difficult to be predetermined and the obtained could be not robust. To overcome this deficiency, we propose a novel algorithm for fast training logistic regression via adaptive sampling. The proposed method decomposes the problem of gradient estimation into several subproblems according to its dimension; then, each subproblem is solved independently by adaptive sampling. Each element of the gradient estimation is obtained by successively sampling a fixed volume training example multiple times until it satisfies its stopping criteria. The final estimation is combined with the results of all the subproblems. It is proved that the obtained gradient estimation is a robust estimation, and it could keep the objective function value decreasing in the iterative calculation. Compared with the representative algorithms using random sampling, the experimental results show that this algorithm obtains comparable classification performance with much less training time.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

Large-scale Motion of Solar Filaments

2000 ◽  
Vol 179 ◽  
pp. 205-208
Author(s):  
Pavel Ambrož ◽  
Alfred Schroll
Keyword(s):  
Magnetic Flux ◽  
Proper Motion ◽  
Time Scale ◽  
Large Scale ◽  
Accuracy Of Measurements ◽  
Solar Filaments ◽  
General Movement ◽  
Scale Motion ◽  
Velocity Scatter

AbstractPrecise measurements of heliographic position of solar filaments were used for determination of the proper motion of solar filaments on the time-scale of days. The filaments have a tendency to make a shaking or waving of the external structure and to make a general movement of whole filament body, coinciding with the transport of the magnetic flux in the photosphere. The velocity scatter of individual measured points is about one order higher than the accuracy of measurements.

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