# Questions tagged [cost-maximization]

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### Transforming a minimization problem to a maximization problem

I have an objective to minimize the transmission delay (D) and energy cost (E) for a wireless network device. While I am solving it using reinforcement learning (Q-Learning to be exact), hence I have ...
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### Calculate the boundary point of the cost of two curves

There is a function which indicates the cost per litre for a heat storage. When buying a larger storage tank, you get it at a lower price per litre. (left plot) In addition, there is a function that ...
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### How to define cost function for custom nonlinear functions?

For logistic regression, the Cost function is defined as: \begin{equation} Cost(h_{\theta}(x)-y) = -ylog(h_{\theta}(x))-(1-y)log(1-h_{\theta}(x)) \end{equation} I now have a nonlinear function \begin{...
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### Hinge loss proof

I hope this doesn't come off as a silly question, but I am looking at SVMs and in principle I understand how they work. The idea is to maximize the margin between different classes of point (within ...
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### Best loss function for very sparse real-valued data

Suppose the target output of my data prediction model is an $M\times N$ matrix where $95\%$ of the values are $0.0$ and the other values are anywhere between $0.0$ and $1.0$, what would be a good loss ...
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### Predictive modelling and cost function

I have to help a company to detect customer in a list of prospects. The company has this benefit/cost function: Value of a new customer = $20 Acquisition cost =$5 So if the model: Miss to detect ...
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### cost function in logistic regression vs optimization algorithms

I have a table like: ...
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### tanh activation function vs sigmoid activation function

The tanh activation function is: $$tanh \left( x \right) = 2 \cdot \sigma \left( 2 x \right) - 1$$ Where $\sigma(x)$, the sigmoid function, is defined as: $$\sigma(x) = \frac{e^x}{1 + e^x}$$. ...
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### why minimize loss function instead of maximizing reward function?

Why is the "de-facto" in statistics to minimize the sum of squared errors cost function instead of maximizing some reward function like the likelihood function?
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### Dissimilarity with earlier features part of cost function

I am using a RandomForest on features (pixels) of images, and I am considering adding cost for "similarity to already other included features" to the cost function. Imagine you have a current RF ...
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### Cost function in cv. glm for a fitted logistic model when cutoff value of the model is not 0.5

I have a logistic model fitted with the following R function: glmfit<-glm(formula, data, family=binomial) A reasonable cutoff value in order to get a good ...
The approximation to the function $max(x)$ can be written as a NOISY-OR as given below: $$max_k(x) = 1-\prod_k(1-x)$$ Are there any way to approximate $min(x)$ ?