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Questions tagged [cost-maximization]

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0
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1answer
33 views

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 ...
1
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0answers
30 views

Second order gradient-based method to locally maximize sum of squares

Non-linear least squares algorithms such as Gauss-Newton allow me to (locally) minimize a sum of squares of residuals (the output of some non-linear function). I.e. locally solve: $$ \mathbf{x} = \arg\...
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0answers
135 views

Maximizing profit, using GAMS?

I've been given the following optimization problem and this is what I have done so far: Cuppa Coffee Company mixes specialty coffee blends to sell to SmartBux, a small chain of coffee shops. The ...
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4answers
2k views

Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point?

Let’s say we have the input (predictor) and output (response) data points A, B, C, D, E and we want to fit a line through the points. This is a simple problem to illustrate the question, but can be ...
2
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0answers
299 views

Why maximizing the lower bound of variational evidence maximizes the probability of observing data

In vaiational bayesian inference we attempt to find a proxy function to best estimate the intractable posterior $P(z|X)$. We define best as the probability distribution that minimizes the KL ...
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2answers
2k views

Why can't this function be used as a loss function?

In a discussion, a friend mentioned that the function below cannot be optimized so it can't be used in a learning algorithm. $$E_{in} = \frac{1}{N} \sum_{n=0}^N (h(x_n) \ne f(x_n))$$ Why can't this ...
1
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2answers
156 views

Why the MSE function has the square?

There is mse function: C = $\frac{1}{2n}$ * $\sum(length(y - a)^2)$ why not just use C = $\sum(length(y - a))$ ? (where "length" is the vector's length, "y" - ideal network's output, "a" - current ...
2
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0answers
29 views

How can I generate the confidence value for each prediction of the target when using cost matrix on C5?

I am using cost matrix for an unbalanced set -70% (not enrolled: 0)-30% (enrolled:1) : error_cost <- matrix(c(0, 1.5, 1, 0), nrow = 2) I have not being able to ...
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0answers
526 views

Should I use a cost matrix or an F2-score?

I'm currently building a classification model in which a false negative is twice as bad as a false positive. That is, the cost matrix looks like (where a leading $P$ means "predicted" and a leading $...
1
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1answer
823 views

Gradient Descent and Cost Function trouble

I am taking Andrew Ng's ML course. I noticed the following: When he is talking about gradient descent with J(theta0, theta1), he "descends" into a negative J(t0, t1) output. Here is a photo: After: ...
0
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1answer
71 views

Designing a hypothesis experiment for cost reduction

I have two groups of samples. Control group and treatment group, treatment group gets a special training. This training will cost me X dollars per person. This training is supposedly reduce the ...
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0answers
557 views

How to compute the gradients for activation maximization in neural network?

I have a question regarding the Activation Maximization technique for neural networks. Activation Maximization is a technique used to visualize the filters of a neural network: Erhan, Dumitru, et al....
3
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2answers
559 views

Why do we use the Unregularized Cost to plot a Learning Curve?

I'm taking Andrew Ng's Machine Learning Course. In the section on determining the variance/bias of your model, he suggests the following. For a given regularization parameter and set of features ...
5
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1answer
808 views

Calculating Costs for ROC Curves

I am trying to calculate the optimal threshold for a binary classifier using Receiver operating characteristic (ROC) Curves. Currently I am assigning a cost for each false negative and another cost ...
0
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1answer
37 views

I would like to know whether expectation maximization is relevant to cost optimization imbalance

I have a cost matrix which has probability confusion matrix Here is the cost predict good-actually good: 0 predict good-actually bad: 3 consequence points (negative) predict bad-actually bad: 0 ...
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0answers
205 views

Confusion with EM Algorithm for Gaussian Mixture?

I am trying to learn EM Algorithm for Gaussian Mixture. But not able to understand few stuffs. This is what I have understood. Consider GMM with k components. $$ p( \mathbf{x}| \mathbf{\alpha_{k}},\...
2
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3answers
1k views

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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0answers
128 views

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 ...
5
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1answer
826 views
77
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2answers
84k views

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: $$\sigma(x) = \frac{e^x}{1 + e^x}$$. ...
2
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3answers
2k views

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?
2
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0answers
97 views

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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2answers
5k views

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 ...
1
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1answer
2k views

approximation to maximum and minimum function : soft-min and soft-max

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 $min(x)$ ?
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4answers
3k views

Supervised learning : How did they find the Cost function to minimize?

I'm studying a tutorial in a video about supervised learning, more specifically, it's about "linear regression with one variable", that is the cost function. So my first question : is this "cost ...