"손실 함수"의 두 판 사이의 차이

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-== 노트 ==
-* The following points highlight the three main types of cost functions.<ref name="ref_fabe">[http://www.economicsdiscussion.net/cost/3-main-types-of-cost-functions/19976 3 Main Types of Cost Functions]</ref>
-* that statistical cost functions will have a bias towards linearity.<ref name="ref_fabe" />
-* We have noted that if the cost function is linear, the equation used in preparing the total cost curve in Fig.<ref name="ref_fabe" />
-* Most economists agree that linear cost functions are valid over the relevant range of output for the firm.<ref name="ref_fabe" />
-* In traditional economics, we must make use of the cubic cost function as illustrated in Fig. 15.5.<ref name="ref_fabe" />
-* However, there are cost functions which cannot be decomposed using a loss function.<ref name="ref_2c4d">[http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/concepts/library_design/losses.html Loss and Cost Functions — Shark 3.0a documentation]</ref>
-* In other words, all loss functions generate a cost function, but not all cost functions must be based on a loss function.<ref name="ref_2c4d" />
-* This allows embarrassingly parallelizable gradient descent on the cost function.<ref name="ref_2c4d" />
-* hasFirstDerivative Can the cost function calculate its first derivative?<ref name="ref_2c4d" />
-* The cost function, , describes how the firm’s total costs vary with its output—the number of cars, , that it produces.<ref name="ref_d624">[https://www.core-econ.org/the-economy/book/text/leibniz-07-03-01.html The Economy: Leibniz: Average and marginal cost functions]</ref>
-* Now think about the shape of the average cost function.<ref name="ref_d624" />
-* A cost function is a MATLAB® function that evaluates your design requirements using design variable values.<ref name="ref_ead5">[https://www.mathworks.com/help/sldo/ug/writing-a-custom-cost-function.html Write a Cost Function]</ref>
-* When you optimize or estimate model parameters, you provide the saved cost function as an input to sdo.optimize .<ref name="ref_ead5" />
-* To understand the parts of a cost function, consider the following sample function myCostFunc .<ref name="ref_ead5" />
-* Value; % Compute the requirements (objective and constraint violations) and % assign them to vals, the output of the cost function.<ref name="ref_ead5" />
-* Specifies the inputs of the cost function.<ref name="ref_ead5" />
-* A cost function must have as input, params , a vector of the design variables to be estimated, optimized, or used for sensitivity analysis.<ref name="ref_ead5" />
-* For more information, see Specify Inputs of the Cost Function.<ref name="ref_ead5" />
-* In this sample cost function, the requirements are based on the design variable x, a model parameter.<ref name="ref_ead5" />
-* The cost function first extracts the current values of the design variables and then computes the requirements.<ref name="ref_ead5" />
-* Specifies the requirement values as outputs, vals and derivs , of the cost function.<ref name="ref_ead5" />
-* A cost function must return vals , a structure with one or more fields that specify the values of the objective and constraint violations.<ref name="ref_ead5" />
-* For more information, see Specify Outputs of the Cost Function.<ref name="ref_ead5" />
-* However, sdo.optimize and sdo.evaluate accept a cost function with only one input argument.<ref name="ref_ead5" />
-* To use a cost function that accepts more than one input argument, you use an anonymous function.<ref name="ref_ead5" />
-* Suppose that the myCostFunc_multi_inputs.m file specifies a cost function that takes params and arg1 as inputs.<ref name="ref_ead5" />
-* For example, you can make the model name an input argument, arg1 , and configure the cost function to be used for multiple models.<ref name="ref_ead5" />
-* You create convenience objects once and pass them as an input to the cost function to reduce code redundancy and computation cost.<ref name="ref_ead5" />
-* We will conclude that theT-policy optimumN andD policies depends on the employed cost function.<ref name="ref_a167">[https://link.springer.com/article/10.1007/BF02888260 A unified cost function for M/G/1 queueing systems with removable server]</ref>
-* What we need is a cost function so we can start optimizing our weights.<ref name="ref_a976">[https://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html Linear Regression — ML Glossary documentation]</ref>
-* Let’s use MSE (L2) as our cost function.<ref name="ref_a976" />
-* To minimize MSE we use Gradient Descent to calculate the gradient of our cost function.<ref name="ref_a976" />
-* Math There are two parameters (coefficients) in our cost function we can control: weight \(m\) and bias \(b\).<ref name="ref_a976" />
-* This applet will allow you to graph a cost function, tangent line to the cost function and the marginal cost function.<ref name="ref_8500">[https://www.geogebra.org/m/Rva9PED2 Cost Functions and Marginal Cost Functions]</ref>
-* The cost is the quadratic cost function, \(C\), introduced back in Chapter 1.<ref name="ref_83c2">[https://eng.libretexts.org/Bookshelves/Computer_Science/Book%3A_Neural_Networks_and_Deep_Learning_(Nielsen)/03%3A_Improving_the_way_neural_networks_learn/3.01%3A_The_cross-entropy_cost_function 3.1: The cross-entropy cost function]</ref>
-* I'll remind you of the exact form of the cost function shortly, so there's no need to go and dig up the definition.<ref name="ref_83c2" />
-* Introducing the cross-entropy cost function How can we address the learning slowdown?<ref name="ref_83c2" />
-* It turns out that we can solve the problem by replacing the quadratic cost with a different cost function, known as the cross-entropy.<ref name="ref_83c2" />
-* In fact, frankly, it's not even obvious that it makes sense to call this a cost function!<ref name="ref_83c2" />
-* Before addressing the learning slowdown, let's see in what sense the cross-entropy can be interpreted as a cost function.<ref name="ref_83c2" />
-* Two properties in particular make it reasonable to interpret the cross-entropy as a cost function.<ref name="ref_83c2" />
-* These are both properties we'd intuitively expect for a cost function.<ref name="ref_83c2" />
-* But the cross-entropy cost function has the benefit that, unlike the quadratic cost, it avoids the problem of learning slowing down.<ref name="ref_83c2" />
-* This cancellation is the special miracle ensured by the cross-entropy cost function.<ref name="ref_83c2" />
-* For both cost functions I simply experimented to find a learning rate that made it possible to see what is going on.<ref name="ref_83c2" />
-* As discussed above, it's not possible to say precisely what it means to use the "same" learning rate when the cost function is changed.<ref name="ref_83c2" />
-* Part of the reason is that the cross-entropy is a widely-used cost function, and so is worth understanding well.<ref name="ref_83c2" />
-* So the log-likelihood cost behaves as we'd expect a cost function to behave.<ref name="ref_83c2" />
-* The average cost function is formed by dividing the cost by the quantity.<ref name="ref_db24">[https://scholarlyoa.com/what-is-an-average-cost-function/ What is an average cost function?]</ref>
-* Cost functions are also known as Loss functions.<ref name="ref_aeab">[https://machinelearningknowledge.ai/cost-functions-in-machine-learning/ Dummies guide to Cost Functions in Machine Learning [with Animation]]</ref>
-* This is where cost function comes into the picture.<ref name="ref_aeab" />
-* weight for the next iteration on training data so that the error given by cost function gets further reduced.<ref name="ref_aeab" />
-* The cost functions for regression are calculated on distance-based error.<ref name="ref_aeab" />
-* This also known as distance-based error and it forms the basis of cost functions that are used in regression models.<ref name="ref_aeab" />
-* In this cost function, the error for each training data is calculated and then the mean value of all these errors is derived.<ref name="ref_aeab" />
-* So Mean Error is not a recommended cost function for regression.<ref name="ref_aeab" />
-* Cost functions used in classification problems are different than what we saw in the regression problem above.<ref name="ref_aeab" />
-* So how does cross entropy help in the cost function for classification?<ref name="ref_aeab" />
-* We could have used regression cost function MAE/MSE even for classification problems.<ref name="ref_aeab" />
-* Hinge loss is another cost function that is mostly used in Support Vector Machines (SVM) for classification.<ref name="ref_aeab" />
-* There are many cost functions to choose from and the choice depends on type of data and type of problem (regression or classification).<ref name="ref_aeab" />
-* error (MSE) and Mean Absolute Error (MAE) are popular cost functions used in regression problems.<ref name="ref_aeab" />
-* We will illustrate the impact of partial updates on the cost function J M ( k ) with two numerical examples.<ref name="ref_fcc2">[https://www.sciencedirect.com/topics/engineering/cost-function-contour Cost Function Contour - an overview]</ref>
-* The cost functions of the averaged systems have been computed to shed some light on the observed differences in convergence rates.<ref name="ref_fcc2" />
-* This indicates that the cost function gets gradually flatter for M -max and is the flattest for sequential partial updates.<ref name="ref_fcc2" />
-* Then given this class definition, the auto differentiated cost function for it can be constructed as follows.<ref name="ref_965b">[http://ceres-solver.org/nnls_modeling.html Modeling Non-linear Least Squares — Ceres Solver]</ref>
-* The algorithm exhibits considerably higher accuracy, but does so by additional evaluations of the cost function.<ref name="ref_965b" />
-* This class allows you to apply different conditioning to the residual values of a wrapped cost function.<ref name="ref_965b" />
-* This class compares the Jacobians returned by a cost function against derivatives estimated using finite differencing.<ref name="ref_965b" />
-* Using a robust loss function, the cost for large residuals is reduced.<ref name="ref_965b" />
-* Here the convention is that the contribution of a term to the cost function is given by \(\frac{1}{2}\rho(s)\), where \(s =\|f_i\|^2\).<ref name="ref_965b" />
-* Ceres includes a number of predefined loss functions.<ref name="ref_965b" />
-* Sometimes after the optimization problem has been constructed, we wish to mutate the scale of the loss function.<ref name="ref_965b" />
-* This can have better convergence behavior than just using a loss function with a small scale.<ref name="ref_965b" />
-* The cost function carries with it information about the sizes of the parameter blocks it expects.<ref name="ref_965b" />
-* This option controls whether the Problem object owns the cost functions.<ref name="ref_965b" />
-* If set to TAKE_OWNERSHIP, then the problem object will delete the cost functions on destruction.<ref name="ref_965b" />
-* The destructor is careful to delete the pointers only once, since sharing cost functions is allowed.<ref name="ref_965b" />
-* This option controls whether the Problem object owns the loss functions.<ref name="ref_965b" />
-* If set to TAKE_OWNERSHIP, then the problem object will delete the loss functions on destruction.<ref name="ref_965b" />
-* The destructor is careful to delete the pointers only once, since sharing loss functions is allowed.<ref name="ref_965b" />
-* * loss_function, double* x0, Ts... xs) Add a residual block to the overall cost function.<ref name="ref_965b" />
-* apply_loss_function as the name implies allows the user to switch the application of the loss function on and off.<ref name="ref_965b" />
-* Users must provide access to pre-computed shared data to their cost functions behind the scenes; this all happens without Ceres knowing.<ref name="ref_965b" />
-* I think it would be useful to have a list of common cost functions, alongside a few ways that they have been used in practice.<ref name="ref_4a94">[https://stats.stackexchange.com/questions/154879/a-list-of-cost-functions-used-in-neural-networks-alongside-applications A list of cost functions used in neural networks, alongside applications]</ref>
-* A cost function is the performance measure you want to minimize.<ref name="ref_0df0">[https://zone.ni.com/reference/en-XX/help/371894J-01/lvsimconcepts/sim_c_costfunc/ Defining a Cost Function (Control Design and Simulation Module)]</ref>
-* The cost function is a functional equation, which maps a set of points in a time series to a single scalar value.<ref name="ref_0df0" />
-* Use the Cost type parameter of the SIM Optimal Design VI to specify the type of cost function you want this VI to minimize.<ref name="ref_0df0" />
-* A cost function that integrates the error.<ref name="ref_0df0" />
-* A cost function that integrates the absolute value of the error.<ref name="ref_0df0" />
-* A cost function that integrates the square of the error.<ref name="ref_0df0" />
-* A cost function that integrates the time multiplied by the absolute value of the error.<ref name="ref_0df0" />
-* A cost function that integrates the time multiplied by the error.<ref name="ref_0df0" />
-* A cost function that integrates the time multiplied by the square of the error.<ref name="ref_0df0" />
-* A cost function that integrates the square of the time multiplied by the square of the error.<ref name="ref_0df0" />
-* After you define these parameters, you can write LabVIEW block diagram code to manipulate the parameters according to the cost function.<ref name="ref_0df0" />
-* However, the reward associated with each reach (i.e., cost function) is experimentally imposed in most work of this sort.<ref name="ref_d68d">[https://jov.arvojournals.org/article.aspx?articleid=2130788 Statistical decision theory for everyday tasks: A natural cost function for human reach and grasp]</ref>
-* We are interested in deriving natural cost functions that may be used to predict people's actions in everyday tasks.<ref name="ref_d68d" />
-* Our results indicate that people are reaching in a manner that maximizes their expected reward for a natural cost function.<ref name="ref_d68d" />
-* Y* one of the parameters of the cost-minimization story, must be included in the cost function.<ref name="ref_6f05">[https://cruel.org/econthought/essays/product/cost.html The Cost Function]</ref>
-* Property (6), the concavity of the cost function, can be understood via the use of Figure 8.2.<ref name="ref_6f05" />
-* We have drawn two cost functions, C*(w, y) and C(w, y), where total costs are mapped with respect to one factor price, w i .<ref name="ref_6f05" />
-* The corresponding cost function is shown in Figure 8.2 by C*(w, y).<ref name="ref_6f05" />
-* , the cost function C(w, y) will lie below the Leontief cost function C*(w, y).<ref name="ref_6f05" />
-* Now, recall that one of the properties of cost functions were their concavity with respect to individual factor prices.<ref name="ref_6f05" />
-* Now, as we saw, ｶ C/ ｶ y ｳ 0 by the properties of the cost function.<ref name="ref_6f05" />
-* As we have demonstrated, the cost function C(w, y) is positively related to the scale of output.<ref name="ref_6f05" />
-* One ought to imagine that the cost function would thus also capture these different returns to scale in one way or another.<ref name="ref_6f05" />
-* The cost function C(w 0 , y) drawn in Figure 8.5 is merely a "stretched mirror image" of the production function in Figure 3.1.<ref name="ref_6f05" />
-* The resulting shape would be similar to the cost function in Figure 8.5.<ref name="ref_6f05" />
-* We can continue exploiting the relationship between cost functions and production functions by turning to factor price frontiers.<ref name="ref_6f05" />
-* Relying on the observation of flexible cost functions is pivotal to successful business planning in regards to market expenses.<ref name="ref_bff8">[https://www.thoughtco.com/cost-function-definition-1147988 What is a Cost Function?]</ref>
-* One of these algorithmic changes was the replacement of mean squared error with the cross-entropy family of loss functions.<ref name="ref_8699">[https://machinelearningmastery.com/loss-and-loss-functions-for-training-deep-learning-neural-networks/ Loss and Loss Functions for Training Deep Learning Neural Networks]</ref>
-* Importantly, the choice of loss function is directly related to the activation function used in the output layer of your neural network.<ref name="ref_8699" />
-* The choice of cost function is tightly coupled with the choice of output unit.<ref name="ref_8699" />
-* A cost function is a mathematical formula used to used to chart how production expenses will change at different output levels.<ref name="ref_4afc">[https://www.myaccountingcourse.com/accounting-dictionary/cost-function What is a Cost Function? - Definition]</ref>
-* Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function.<ref name="ref_3e49">[https://www.kdnuggets.com/2020/05/5-concepts-gradient-descent-cost-function.html 5 Concepts You Should Know About Gradient Descent and Cost Function]</ref>
-* Let’s use supervised learning problem ; linear regression to introduce model, cost function and gradient descent.<ref name="ref_983b">[https://medium.com/@dhartidhami/machine-learning-basics-model-cost-function-and-gradient-descent-79b69ff28091 Machine Learning Basics: Model, Cost function and Gradient Descent]</ref>
-* Also, as it turns out the gradient descent for the cost function for linear regression is a convex function.<ref name="ref_983b" />
-* An optimization problem seeks to minimize a loss function.<ref name="ref_7bae">[https://en.wikipedia.org/wiki/Loss_function Loss function]</ref>
-* The use of a quadratic loss function is common, for example when using least squares techniques.<ref name="ref_7bae" />
-* The quadratic loss function is also used in linear-quadratic optimal control problems.<ref name="ref_7bae" />
-* In ML, cost functions are used to estimate how badly models are performing.<ref name="ref_3099">[https://towardsdatascience.com/machine-learning-fundamentals-via-linear-regression-41a5d11f5220 Machine learning fundamentals (I): Cost functions and gradient descent]</ref>
-* At this point the model has optimized the weights such that they minimize the cost function.<ref name="ref_3099" />
-* Cost Function quantifies the error between predicted values and expected values and presents it in the form of a single real number.<ref name="ref_0625">[https://towardsdatascience.com/coding-deep-learning-for-beginners-linear-regression-part-2-cost-function-49545303d29f Coding Deep Learning for Beginners — Linear Regression (Part 2): Cost Function]</ref>
-* Depending on the problem Cost Function can be formed in many different ways.<ref name="ref_0625" />
-* The goal is to find the values of model parameters for which Cost Function return as small number as possible.<ref name="ref_0625" />
-* let’s try picking smaller weight now and see if the created Cost Function works.<ref name="ref_0625" />
-===소스===
- <references />

"손실 함수"의 두 판 사이의 차이

2020년 12월 15일 (화) 23:38 판

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