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Loss Functions for Loss Estimation
The Annals of Statistics, 1988 Let X be a random variable with distribution \(P_{\theta}\), where \(\theta\in \Theta\), and d(X) a decision for \(\theta\) with loss W(\(\theta\),d(X)). A class of loss functions combining the decision error W(\(\theta\),d(X)) and the error in estimating W(\(\theta\),d(X)) by h(X) is introduced. Under these loss functions the Bayes procedure \((d_ B(X)
openaire +3 more sources
Handling negative mentions on social media channels using deep learning
Journal of Information and Telecommunication, 2019 Social media channels such as social networks, forum or online blogs have been emerging as major sources from which brands can gather user opinions about their products, especially the negative mentions.
Khuong Vo +6 more
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Mask RCNN-based Single Shot Multibox Detector For Gesture Recognition In Physical Education
Journal of Applied Science and Engineering, 2022 Human-computer interaction (HCI) is an important supporting technology in the computer vision area, especially in physical education. HCI can promote the efficiency of physical education class, which is of great help to improve the learning efficiency ...
Tao Feng
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Evaluation of RPL’s Single Metric Objective Functions [PDF]
, 2017 In this paper, we evaluate the performance of RPL (IPv6 Routing Protocol for Low Power and Lossy Networks) based on the Objective Function being used to construct the Destination Oriented Directed Acyclic Graph (DODAG).
Alayed, Walaa +2 more
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Applied Sciences, 2023 A memory-type control chart is an important tool of statistical process control for monitoring small to moderate shifts in the manufacturing process. Using the prior information by the Bayesian approach is helpful in control charts.
Imad Khan +5 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Training deep neural networks is inherently subject to the predefined and fixed loss functions during optimizing. To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and
Qingliang Liu, Jinmei Lai
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Primary lung carcinomas and bronchial carcinoid tumors (BC) are very rare malignancies in childhood. While typical BC and mucoepidermoid carcinomas are mostly low‐grade, localized tumors with a more favorable prognosis than in adults, necessitating avoidance of overtreatment, adenocarcinomas of the lung are often diagnosed at advanced disease ...
Michael Abele +19 more
wiley +1 more source
Competitive on-line learning with a convex loss function [PDF]
, 2005 We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on the following binary observation. In competitive on-line learning, the goal is to design decision algorithms that are almost as good
Vovk, Vladimir
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The Population of Dark Matter Subhaloes: Mass Functions and Average Mass Loss Rates
, 2007 Using a cosmological N-Body simulation and a sample of re-simulated cluster-like haloes, we study the mass loss rates of dark matter subhaloes, and interpret the mass function of subhaloes at redshift zero in terms of the evolution of the mass function ...
Carlo Giocoli +8 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Although most malignant germ cell tumors (GCTs) are highly curable with cisplatin‐based therapy, options for patients with multiply relapsed/refractory disease remain limited. For this population, we report the first pediatric use of gemcitabine, docetaxel, melphalan, and carboplatin (GemDMC) as part of sequential cycles of high‐dose ...
Maria Frost +10 more
wiley +1 more source