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- ...tic loss (orange), Exponential loss (purple), Tangent loss (brown), Square loss (blue)]] ...ef> As a result, the goal of the learning problem is to minimize expected loss (also known as the risk), defined as ...24 KB (3,729 words) - 20:04, 6 December 2024
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- ..., for instance, a machinist exceeds a rigid blueprint tolerance. Instead 'loss' in value progressively increases as variation increases from the intended The concept of Taguchi's quality loss function was in contrast with the American concept of quality, popularly kn ...3 KB (498 words) - 21:08, 5 October 2020
- ...xville, Tennessee, USA|pages=17–25}}</ref> the extension makes use of loss functions to derive '''<math>\textstyle \alpha</math>''' and '''<math>\textstyle \bet ...style [x]</math>. <math>\textstyle \lambda(a_i\mid w_j)</math> denotes the loss, or cost, for performing action <math>\textstyle a_i</math> when the state ...7 KB (1,117 words) - 13:58, 5 March 2024
- {{Short description|Loss function used in robust regression}} ...], that is less sensitive to [[outlier]]s in data than the [[squared error loss]]. A variant for classification is also sometimes used. ...8 KB (1,189 words) - 01:43, 21 November 2024
- ...glorot11a.pdf |conference=AISTATS |quote=Rectifier and softplus activation functions. The second one is a smooth version of the first. }}</ref> and ''SmoothReLU ==Related functions== ...5 KB (688 words) - 12:43, 7 October 2024
- ...Boost algorithm minimizes the [[Loss functions for classification|logistic loss]]: ...2 KB (202 words) - 08:43, 11 December 2024
- {{Short description|Loss function in machine learning}} ...ents the value of the prediction {{mvar|y}}. The plot shows that the Hinge loss penalizes predictions {{math|''y'' < 1}}, corresponding to the notion of a ...8 KB (1,205 words) - 15:32, 9 August 2024
- ...er Jordan, Fabian Krüger, "Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings," [https://arxiv.org/pdf/15 [[Quantile regression]] minimizes an asymmetric <math>L_1</math> loss (see [[least absolute deviations]]). ...2 KB (281 words) - 02:28, 12 November 2024
- ...called '''Riesz–Sobolev''' inequality, states that any three non-negative functions <math>f : \mathbb{R}^n \to \mathbb{R}^+</math>, <math>g : \mathbb{R}^n \to ...\mathbb{R}^+</math> are the [[symmetric decreasing rearrangement]]s of the functions <math>f</math>, <math>g</math> and <math>h</math> respectively. ...4 KB (521 words) - 20:24, 27 April 2024
- ...plet loss''' is a [[machine learning]] [[Loss functions for classification|loss function]] widely used in [[One-shot learning (computer vision)|one-shot le [[File:Triplet_Loss_Minimization.png|thumb|300x300px|The triplet loss function minimizes the distance between an anchor and a positive, both of w ...8 KB (1,249 words) - 21:02, 23 February 2025
- ...the [[training set]] data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids [[overfit ...|author3=Caponnetto A. |author4=Piana M. |author5=Verri A. |title=Are Loss Functions All the Same |journal=Neural Computation |date=May 2004 |volume=16 |issue=5 ...10 KB (1,465 words) - 08:02, 6 June 2024
- ...on-life/casualty [[actuarial science]], using various forms of mean excess functions {{harv|Benktander|Segerdahl|1960}}. The distribution of the first type is " ...Representation of Claim Distributions with Special Reference to Excess of Loss Reinsurance ...3 KB (315 words) - 21:02, 27 June 2024
- ...tic loss (orange), Exponential loss (purple), Tangent loss (brown), Square loss (blue)]] ...ef> As a result, the goal of the learning problem is to minimize expected loss (also known as the risk), defined as ...24 KB (3,729 words) - 20:04, 6 December 2024
- ...on-life/casualty [[actuarial science]], using various forms of mean excess functions {{harv|Benktander|Segerdahl|1960}}. This distribution is "close" to the [[W ...Representation of Claim Distributions with Special Reference to Excess of Loss Reinsurance ...3 KB (389 words) - 01:06, 11 April 2021
- ...ersing a [[scintillator]], and gives a relation that is not linear at high loss rates. ...ized molecules at a point along the particle's path to the specific energy loss;<ref name="Birks 1951" /> "Since ''k'' and ''B'' appear only as a product, ...5 KB (669 words) - 07:03, 6 February 2025
- ...d <math>g</math> are nonnegative [[measurable function|measurable]] [[real functions]] vanishing at [[infinity]] that are defined on <math>n</math>-[[dimension] ...authorlink1=Elliott H. Lieb|last2=Loss|first2=Michael|author2-link=Michael Loss|title=Analysis|year=2001|edition=2nd|publisher=[[American Mathematical Soci ...5 KB (904 words) - 20:25, 27 April 2024
- ...{{math|''f''<sub>''n''</sub>}} be a sequence of measurable complex-valued functions on {{mvar|X}} which converge almost everywhere to a function {{mvar|f}}. Th ...3|1loc=Theorem 2|2a1=Bogachev|2y=2007|2loc=Proposition 4.7.30|3a1=Lieb|3a2=Loss|3y=2001|3loc=Theorem 1.9}} A significant consequence, which sharpens [[Fato ...4 KB (660 words) - 07:31, 18 February 2025
- ...ng [[function (mathematics)|function]]s ''f''<sub>A<sub>i</sub></sub>, and loss, which is a limit on the percentage of [[tuple]]s that can be suppressed. P '''Assumes''': | PT | ≤ ''k'', and loss * | PT | = ''k'' ...5 KB (710 words) - 23:35, 9 December 2023
- ...iness involves backlog issues such as customer compensation for delays and loss of goodwill. Earliness incurs expenses for storage of the manufactured item In a large number of cases the problems of optimizing these functions are [[NP-hard]].<ref>[http://www2.informatik.uni-osnabrueck.de/knust/class/ ...2 KB (381 words) - 01:53, 17 July 2023
- ...authorlink1=Elliott H. Lieb|last2=Loss|first2=Michael|author2-link=Michael Loss|title=Analysis|year=2001|edition=2nd|publisher=[[American Mathematical Soci ==Definition for functions== ...7 KB (1,081 words) - 23:34, 9 April 2023
- ...[vacuum]].<ref name="Lieb">{{cite book |last1= Lieb|first1= Elliott|last2= Loss|first2= Michael|date= 2001|title= Analysis |location= Providence|publisher= ...d <math>H^1(\mathbb R^n)</math> the [[Sobolev space]] of square-integrable functions with square-integrable derivatives. ...5 KB (791 words) - 11:44, 22 January 2025