Power mean SVM for large scale visual classification

2012 ◽  
Author(s):  
Jianxin Wu
Keyword(s):  
Large Scale ◽  
Power Mean ◽  
Visual Classification

Large scale classifiers for visual classification tasks

2014 ◽  
Vol 74 (4) ◽  
pp. 1199-1224 ◽  
Author(s):  
Thanh-Nghi Doan ◽  
Thanh-Nghi Do ◽  
François Poulet
Keyword(s):  
Large Scale ◽  
Visual Classification ◽  
Classification Tasks

scholarly journals Optimization and Validation of a Linear Appraisal Scoring System for Milk Production-Linked Zoometric Traits in Murciano-Granadina Dairy Goats and Bucks

2020 ◽  
Vol 10 (16) ◽  
pp. 5502 ◽  
Author(s):  
Javier Fernández Álvarez ◽  
Jose Manuel León Jurado ◽  
Francisco Javier Navas González ◽  
Carlos Iglesias Pastrana ◽  
Juan Vicente Delgado Bermejo
Keyword(s):  
Large Scale ◽  
Predictive Power ◽  
Cross Validation ◽  
Information Criteria ◽  
Model Fit ◽  
Power Mean ◽  
Dairy Goats ◽  
High Internal Consistency ◽  
Appraisal Systems ◽  
Better Than

Implementing linear appraisal systems (LAS) may reduce time, personnel and resource costs when performing large-scale zoometric collection. However, optimizing complex zoometric variable panels and validating the resulting reduced outputs may still be necessary. The lack of cross-validation may result in the loss of accuracy and value of the practices implemented. Special attention should be paid when zoometric panels are connected to economically-relevant traits such as dairy performance. This methodological proposal aims to optimize and validate LAS in opposition to the traditional measuring protocols routinely implemented in Murciano-Granadina goats. The sample comprises 41,323 LAS and traditional measuring records from 22,727 herdbook-registered primipara does, 17,111 multipara does and 1485 bucks. Each record includes information on 17 linear traits for primipara/multipara does and 10 traits for bucks. All zoometric parameters are scored on a nine-point scale. Cronbach’s alpha values suggest a high internal consistency of the optimized variable panels. Model fit, variability explanation power and predictive power (mean square error (MSE), Akaike (AIC)/corrected Akaike (AICc) and Bayesian information criteria (BIC), respectively) suggest the model comprising zoometric LAS scores performs better than traditional zoometry. Optimized reduced models are able to capture variability for dairy-related zoometric traits without noticeable detrimental effects on model validity properties.

scholarly journals Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Yu Wang ◽  
Qinghua Hu ◽  
Pengfei Zhu ◽  
Linhao Li ◽  
Bingxu Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Visual Classification

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.

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