Questions tagged [non-linear-regression]
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. It is used in place when the data shows a curvy trend, and linear regression would not produce very accurate results when compared to non-linear regression.
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Does a second-order fully-connected layer have any uses?
I was thinking about implementing second-order regression via a fully-connected layer, and I came up with this:
$X$ is the input data, shaped $(features, batch\_number)$.
$w0$ is the bias, shaped $(...
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Neural Network for Picking Parameters of a Nonlinear Function to Data Points
I'm trying to make a neural network in pytorch that picks the parameters of a nonlinear function, the radius and (x,y) center of a circle in the example below, based on a sample of values from the ...
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What is the difference between neural networks and other ways of curve fitting?
For simplicity, let's assume we want to solve a regression problem, where we have one independent variable and one dependent variable, which we want to predict. Let's also assume that there is a ...
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When are multiple hidden layers necessary?
I know that my question probably seems like being asked many times, but Ill try
to be more speciffic:
Limitations to my question:
I am NOT asking about convolutional neural networks, so please, try ...
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What ML algorithm should I use that suits this data?
What if I have some data, let's say I'm trying to answer if education level and IQ affect earnings, and I want to analyze this data and put in a regression model to predict earnings based on the IQ ...
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If features are always positives, why do we use RELU activation functions?
When does it happen that a layer (either first or hidden) outputs negative values in order to justify the use of RELU?
As far as I know, features are never negative or converted to negative in any ...
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solving xor function using a neural network with no hidden layers
xor is a non-linear dataset. It cannot be solved with any number of perceptron based neural network but when the perceptions are applied the sigmoid activation function, we can solve the xor dataset.
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Regularization of non-linear parameters?
I was wondering whether it is possible to regularize (L1 or L2) non-linear parameters in a general regression model. Say, I have the following non-linear least squares cost function, where $p$ is a $...
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What is the difference between linear and non-linear regression?
In machine learning, I understand that linear regression assumes that parameters or weights in equation should be linear. For Example:
$$y = w_1x_1 + w_2x_2$$
is a linear equation where $x_1$ and $...
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Which AI technique is best suited to discovering non-linear relationships in data?
I am interested in exploring whether AI techniques can derive hidden patterns of relationships in a data set. For example, from among house size, lot size, age of house and asking price, what formula ...
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Is Deep Learning the repeated application of Linear Regression?
Is Deep Learning the repeated application of Linear Regression?
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