scholarly journals Diversity With Cooperation: Ensemble Methods for Few-Shot Classification

2019 ◽  
Author(s):  
Nikita Dvornik ◽  
Julien Mairal ◽  
Cordelia Schmid
Keyword(s):  
Ensemble Methods ◽  
Shot Classification

Actual issues of systematization of scientific knowledge on the conditions of shot in the forensic ballistics

2020 ◽  
Vol 2020 (3) ◽  
pp. 164-169
Author(s):  
Igor' Latyshov ◽  
Fedor Samuylenko
Keyword(s):  
Scientific Knowledge ◽  
Communication Systems ◽  
Production Method ◽  
Natural Phenomenon ◽  
Projectile Energy ◽  
Energy Characteristics ◽  
Shot Classification ◽  
Forensic Ballistics

In this research, there was considered a challenge of constructing a system of scientific knowledge of the shot conditions in judicial ballistics. It was observed that there are underlying factors that are intended to ensureits [scientific knowledge] consistency: identification of the list of shot conditions, which require consideration when solving expert-level research tasks on weapons, cartridges and traces of their action; determination of the communication systems in the course of objects’ interaction, which present the result of exposure to the conditions of the shot; classification of the shot conditions based on the grounds significant for solving scientific and practical problems. The article contains the characteristics of a constructive, functional factor (condition) of weapons and cartridges influence, environmental and fire factors, the structure of the target and its physical properties, situational and spatial factors, and projectile energy characteristics. Highlighted are the forms of connections formed in the course of objects’ interaction, proposed are the author’s classifications of forensically significant shooting conditions with them being divided on the basis of the following criteria: production from the object of interaction, production from a natural phenomenon, production method, results weapon operation and utilization, duration of exposure, type of structural connections between interaction objects, number of conditions that apply when firing and the forming traces.

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

scholarly journals Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

2021 ◽  
Vol 13 (2) ◽  
pp. 238
Author(s):  
Zhice Fang ◽  
Yi Wang ◽  
Gonghao Duan ◽  
Ling Peng
Keyword(s):  
Decision Trees ◽  
Landslide Susceptibility ◽  
Ensemble Methods ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges Reservoir Area ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Rotation Forest ◽  
Predictive Values ◽  
Ensemble Technique

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

Prioritization of water erosion–prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran

2021 ◽  
Author(s):  
Hamid Reza Pourghasemi ◽  
Fatemeh Honarmandnejad ◽  
Mahrooz Rezaei ◽  
Mohammad Hassan Tarazkar ◽  
Nitheshnirmal Sadhasivam
Keyword(s):  
Ensemble Methods ◽  
Water Erosion ◽  
Southern Iran

scholarly journals Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 216 ◽  
Author(s):  
Jianjia Wang ◽  
Xichen Wu ◽  
Mingrui Li ◽  
Hui Wu ◽  
Edwin Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ensemble Methods ◽  
Brain Regions ◽  
Fmri Data ◽  
Thermal Systems ◽  
Network Construction ◽  
Network Characteristics ◽  
Network Entropy ◽  
Canonical Ensembles

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

scholarly journals Analog Ensemble Methods for Improving Satellite-Based Intensity Estimates of Tropical Cyclones

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 830
Author(s):  
William E. Lewis ◽  
Timothy L. Olander ◽  
Christopher S. Velden ◽  
Christopher Rozoff ◽  
Stefano Alessandrini
Keyword(s):  
North Atlantic ◽  
Mean Absolute Error ◽  
Ensemble Methods ◽  
Absolute Error ◽  
Eastern Pacific ◽  
Intensity Maximum ◽  
The North ◽  
The University ◽  
Meteorological Satellite ◽  
Fold Cross Validation

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.

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