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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