Questions tagged [objective-function]
The objective-function tag has no usage guidance.
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Scores of the objective function are very close to zero
For my model, I am using a square loss for the objective function. When I get the result of the prediction, the score given for each instance is very close to zero ($1*e^{-10}$). Does this mean that ...
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The principle and understanding of adversarial training
In the paper《ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION》and its related papers.
The researcher apply the adversarial perturbation to word embeddings.
Why do this methods ...
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Deeplearning without an objective function?
In this article, the author talks about how deeplearning models no longer are trained for an objective function that humans specify, but find their own objective function. Specifically, he is talking ...
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Non-linear Regression
For example suppose I've data set which looks like:
[[x,y,z],
[1,2,5],
[2,3,8],
[4,5,14]]
It's easy to find the theta parameters from those tiny data set. ...
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How to determine the function is linear in linear regression problem?
I know that the first degree of the polynomial equation is considered as a linear function.
But, I found some things confusing in linear regression.
...
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Linear regression space transformation
Can someone help me how space transformation works on linear regression problems because I have been confused.
When we perform space transformation with a function e.g. $\varphi (x)$ we perform the ...
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Newbie: Objective Function
I am reading the book "Data Science for Business" by Foster Provost & Tom Fawcett. Only a fourth of the way through.
I am unclear about the concept of Objective Function. I will nevertheless take ...
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3answers
42 views
can machine learning/Deep learning used to minimize an objective function?
I have data of construction site and am wondering if i can use machine learning to reduce the cost it takes to build a building. But, as far as i know, Machine learning can only does function ...
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neural network function approximation with constraints
I would like to approximate a function $f(\cdot)$ by means of a neural network given a finite set of observations $f(x_i)$ where $x_i\in\mathbb{R}^n$ and $i=1\dots,N$. However, I have some prior ...
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What is a good objective function for allowing close to 0 predictions?
Let's say we want to predict the probability of rain. So just the binary case: rain or no rain.
In many cases it makes sense to have this in the [5%, 95%] interval. And for many applications this ...
