Newest 'regression' Questions

2

votes

1answer

42 views

Finding the optimal combination of inputs which return maximal output

I am currently working on a problem and now got stuck to implement one of it's steps. This is a simple attempt to explain what I am currently facing, which is something that I am aiming to implement ...

machine-learning regression

asked Dec 18 '19 at 16:05

Marc Schwambach

412 bronze badges

2

votes

1answer

43 views

Regression using neural network

I'd like to ask for any kind of assistance regarding the following problem: I was given the following training data: 100 numbers, each one is a parameter, they together define a number X(also given)....

asked Dec 11 '19 at 19:12

Grimnast

211 bronze badge

1

vote

0answers

17 views

Optimisation of dependence of efficiency of CNN on training data

I got a large dataset of images (dimensions of 16 x 16, 250k samples) and corresponding spherical coordinates (distributed uniformly in each coordinate). On these, I trained a convolutional regression ...

deep-learning convolutional-neural-networks regression

asked Dec 6 '19 at 6:15

Solvalou

1112 bronze badges

1

vote

0answers

33 views

How to perform regression with multiple numeric (positive and negative) inputs and one numeric output?

I have a dataset with different types of numerical values (both negative and positive numerical values) for the inputs (for example, -40, -35, 1, 25, 39, etc., that is, multiple inputs) and single ...

machine-learning ai-design tensorflow keras regression

asked Dec 5 '19 at 20:15

Rizwan Altaf

132 bronze badges

2

votes

0answers

16 views

Which algorithm and architecture to use for 1:1 matrix transformation of an 8X8 dimension?

I would like to map the simplest 8X8 matrices, one to one, but am not sure which AI algorithm would give the best performance. I am thinking about the DeepLearning4j, however, I don't know which ...

machine-learning deep-learning regression rule-acquisition

asked Dec 1 '19 at 4:16

Damir Olejar

1334 bronze badges

2

votes

0answers

13 views

What is a good model for regression problem with binary features and small data?

I am trying to predict the solution time for riddles in which matchsticks are combined into digits and operators. An example of a matchstick riddle is 4-2=8. The solution for this riddle would be ...

models regression

asked Nov 21 '19 at 14:50

Yitzhak Spielberg

211 bronze badge

2

votes

0answers

23 views

Choosing neural network output for prediction (regression) of a dynamical system

I’m trying to train a neural network to approximate the output of a dynamical system $dy/dt=f\left(y(t), u(t) \right)$, namely, given $y(0)$ and $u(t_i), i=1,2...N$ I want the network to predict $y(...

neural-networks machine-learning prediction regression time-series

asked Nov 14 '19 at 12:13

Ken Grimes

814 bronze badges

2

votes

1answer

45 views

Imposing physical constraints (previous knowledge) in a neural network for regression

I'm trying to train a neural network to do a multiple non-linear regression $y=f(x_i), i=1,2…N$. So far it works good (low MSE), but some predictions $y$ are “non-physical”, for instance for our ...

neural-networks machine-learning regression

asked Nov 14 '19 at 10:41

Ken Grimes

814 bronze badges

2

votes

1answer

44 views

Pose estimation using CNNs on Point clouds

In the case of single shot detection of point clouds, that is the point cloud of an object is taken only from one camera view without any registration. Can a Convolutional Network estimate the 6d pose ...

deep-learning convolutional-neural-networks object-recognition object-detection regression

asked Nov 13 '19 at 13:12

Mostafa Said

335 bronze badges

4

votes

1answer

62 views

What is the best approach for multivariable and multivariate regression?

I want to build a multivariable and multivariate regression model in Keras (with TensorFlow as backend), that is, a regression model with multiple values as input (multivariable) and output (...

machine-learning keras regression

asked Oct 21 '19 at 3:28

Riz

734 bronze badges

0

votes

2answers

53 views

How do we choose the activation function for each hidden node? [duplicate]

I am new to neural networks. I would like to use them as a fitting or forecasting method. A simple NN model that does not contain hidden layers, that is, the input nodes are directly connected to the ...

neural-networks linear-regression hidden-layers regression forecasting

asked Oct 12 '19 at 10:52

Nizar

1012 bronze badges

2

votes

0answers

17 views

How to choose the suitable Neural Network Architecture for Regression Tasks

so I'm working on a Project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration etc.. now the data that I have comes also with a timestamp for each sample ( I ...

neural-networks deep-learning python prediction regression

asked Oct 9 '19 at 15:31

basilisk

1814 bronze badges

0

votes

1answer

52 views

TF Keras: How to turn this probability-based classifier into single-output-neuron label-based classifier

Here's a simple image classifier implemented in TensorFlow Keras (right click to open in new tab): https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/...

classification tensorflow keras loss-functions regression

asked Oct 4 '19 at 8:32

datdinhquoc

4512 silver badges10 bronze badges

2

votes

2answers

52 views

How to express accuracy of a regression ANN that uses MSE loss function?

I have a regression MLP network with all input values between 0 and 1, and am using MSE for the loss function. The minimum MSE over the validation sample set comes to 0.019. So how to express the '...

neural-networks machine-learning regression

asked Sep 13 '19 at 12:04

samiant

411 bronze badge

0

votes

1answer

42 views

How many hidden layers are needed for this training data set

I'm trying to separate classes in 3D space, the data are as in the sketch below: There are 3 classes: 0,1,2; and with the look into the sketch, it seems that I need 3 planes to separate the classes, ...

neural-networks machine-learning deep-network hidden-layers regression

asked Sep 10 '19 at 5:18

datdinhquoc

4512 silver badges10 bronze badges

2

votes

0answers

21 views

Can two neural networks be better instead of one with a categorical feature?

Let's assume, that I have a neural network with few numerical features and one binary categorical feature. The network in this case is used for regression. I wonder if such a neural network can ...

neural-networks regression categorical-data

asked Sep 9 '19 at 20:00

Makintosz

6161 silver badge12 bronze badges

2

votes

0answers

21 views

Confidence Maps and Non-Linearity

I am currently trying to improve a CNN architecture that was proposed for generating depth images. The architecture was originally proposed for autonomous driving and it looks like following : The ...

convolutional-neural-networks computer-vision regression image-segmentation

asked Sep 8 '19 at 5:59

3yanlis1bos

1214 bronze badges

1

vote

1answer

43 views

Have neural networks something to offer which goes beyond regression analysis?

Neural networks are perceived as a powerful regression tool. If a dataset contains of input/output relations, the neural network can adjust it's internal parameters to interpolate the missing data. In ...

neural-networks regression

asked Aug 29 '19 at 14:50

Manuel Rodriguez

2,4911 gold badge5 silver badges29 bronze badges

2

votes

1answer

39 views

Can supervised learning be recast as reinforcement learning problem?

Let's assume that there is a sequence of pairs $(x_i, y_i), (x_{i+1}, y_{i+1}), \dots$ of observations and corresponding labels. Let's also assume that the $x$ is considered as independent variable ...

reinforcement-learning comparison supervised-learning function-approximation regression

asked Aug 27 '19 at 21:50

TomR

6752 silver badges8 bronze badges

1

vote

0answers

77 views

Polynomial Regressor vs Neural Network Regressor

So as far as my knowledge (might be a bit vague and not mathematical) goes a Neural Network can and should only be able to approximate a bounded function, which is not the case of a Polynomial ...

neural-networks regression

asked Aug 11 '19 at 9:21

DuttaA

4,2102 gold badges12 silver badges38 bronze badges

1

vote

0answers

20 views

Literature on Sequence Regresssion

I have some rated time-sequential data and I would like to test if an ANN can learn a correlation between my measurements and ratings. I suspect I could just try a CNN where 1 Dimension is time or an ...

convolutional-neural-networks recurrent-neural-networks sequence-modelling regression

asked Aug 5 '19 at 14:23

jaaq

1584 bronze badges

0

votes

0answers

33 views

Not able to properly tune Neural Network via Back Propagation properly

I have a custom code Neural Network(not using keras or any package...Trying to learn the essence of Neural Network from scratch)... Code can be found here I have the per iteration training output(<...

neural-networks training backpropagation artificial-neuron regression

asked Aug 4 '19 at 17:03

Fr_nkenstien

3652 silver badges8 bronze badges

1

vote

0answers

42 views

What are the possible neural network architecture for linear regression or time series regression?

I started modeling a linear regression problem using dense layers (layers.dense), which works fine. I am really excited, and now I am trying to model a time series linear regression problem using CNN, ...

neural-networks keras regression time-series

asked Jul 29 '19 at 12:16

Jun Liu

191 bronze badge

3

votes

1answer

53 views

What is the relationship between degrees of freedom and the size of the training dataset?

I am going through the book Pattern Recognition by Bishop. At one point he says For $M = 9$, the training set error goes to zero, as we might expect because this polynomial contains 10 degrees of ...

machine-learning comparison regression

asked Jul 25 '19 at 9:50

sage76

1054 bronze badges

2

votes

1answer

14 views

Should I model a problem with quantised output as classification or regression?

Say I have some data I am trying to learn, and I'm aware that the output is quantised in some way, e.g. I can get only get discrete values (0.1, 0.2, 0.3...0.9) in a finite range. Would you treat ...

classification models regression

asked Jul 22 '19 at 12:36

Matt Hamilton

2531 silver badge5 bronze badges

0

votes

1answer

32 views

How can I perform multivariable regression with neural networks?

I want to use a neural network to perform a multivariable regression, where my dataset contains multiple features, but I can't for the life of me figure it out. Every kind of tutorial on the internet ...

neural-networks python regression

asked Jul 16 '19 at 20:42

Carter Mitchell

533 bronze badges

3

votes

1answer

66 views

Which models accept numerical parameters and produce a numerical output?

I need a model that will take in a few numerical parameters, and give back a numerical answer (Context: predicting a slope based on environmental factors without having to actually take measurements ...

machine-learning python definitions regression

asked Jul 12 '19 at 19:21

Carter Mitchell

533 bronze badges

0

votes

0answers

20 views

why my regression model predict every datapoint to the same value

I am trying to train a SVR but I found that with some combination of features, the trained SVR predict every point in test set to the same value. this problem occurs much more when I use linear kernel ...

machine-learning svm regression

asked Jun 22 '19 at 6:55

rashford10

1

0

votes

1answer

35 views

Decide Number of input Parameters and Output Parameters - ANN

I have to create a Neural Network for regression purpose. Basically, I created a Model which predict next 5 values when we give past 6 values. I want to make a change in this neural network. For ...

neural-networks datasets artificial-neuron regression

asked Jun 21 '19 at 7:18

Majo_Jose

1558 bronze badges

2

votes

4answers

109 views

How is regression machine learning?

In regression, in order to minimize an error function, a functional form of hypothesis $h$ must be decided upon, and it must be assumed (as far as I'm concerned) that $f$, the true mapping of instance ...

machine-learning linear-regression supervised-learning regression

asked Jun 7 '19 at 16:42

sangstar

1312 bronze badges

0

votes

0answers

16 views

Auto Regression Predicting large negative number

I am using Auto Regression for prediction Library: statsmodel Sample Python Code: ...

python statistical-ai forecasting regression

asked Jun 6 '19 at 10:52

Majo_Jose

1558 bronze badges

2

votes

1answer

977 views

Why is the hyperbolic tangent with MSE better than the sigmoid with cross-entropy?

Usually, in binary classification problems, we use sigmoid as the activation function of the last layer plus the binary cross-entropy as cost function. However, I have already experienced (more than ...

neural-networks classification comparison loss-functions regression

asked Feb 28 '18 at 14:52

Arnaldo Gualberto

918 bronze badges

7

votes

2answers

206 views

Which predictive algorithm can be used to predict a number given other numbers?

I am currently searching for a supervised learning algorithm that can be used to predict the output given a large enough training set. Here's a simple example. Suppose the training dataset is ...

machine-learning ai-design prediction regression

asked Aug 15 '17 at 15:07

Cryptonaut

812 bronze badges

9

votes

3answers

582 views

Do I need classification or regression to predict the availability of a user given some features?

While studying data mining methods I have come to understand that there are two main categories: Predictive methods: Classification Regression Descriptive methods: Clustering Association rules ...

machine-learning ai-design classification prediction regression

asked Apr 1 '17 at 14:19

Guest2000

1953 bronze badges

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Questions tagged [regression]

Finding the optimal combination of inputs which return maximal output

Regression using neural network

Optimisation of dependence of efficiency of CNN on training data

How to perform regression with multiple numeric (positive and negative) inputs and one numeric output?

Which algorithm and architecture to use for 1:1 matrix transformation of an 8X8 dimension?

What is a good model for regression problem with binary features and small data?

Choosing neural network output for prediction (regression) of a dynamical system

Imposing physical constraints (previous knowledge) in a neural network for regression

Pose estimation using CNNs on Point clouds

What is the best approach for multivariable and multivariate regression?

How do we choose the activation function for each hidden node? [duplicate]

How to choose the suitable Neural Network Architecture for Regression Tasks

TF Keras: How to turn this probability-based classifier into single-output-neuron label-based classifier

How to express accuracy of a regression ANN that uses MSE loss function?

How many hidden layers are needed for this training data set

Can two neural networks be better instead of one with a categorical feature?

Confidence Maps and Non-Linearity

Have neural networks something to offer which goes beyond regression analysis?

Can supervised learning be recast as reinforcement learning problem?

Polynomial Regressor vs Neural Network Regressor

Literature on Sequence Regresssion

Not able to properly tune Neural Network via Back Propagation properly

What are the possible neural network architecture for linear regression or time series regression?

What is the relationship between degrees of freedom and the size of the training dataset?

Should I model a problem with quantised output as classification or regression?

How can I perform multivariable regression with neural networks?

Which models accept numerical parameters and produce a numerical output?

why my regression model predict every datapoint to the same value

Decide Number of input Parameters and Output Parameters - ANN

How is regression machine learning?

Auto Regression Predicting large negative number

Why is the hyperbolic tangent with MSE better than the sigmoid with cross-entropy?

Which predictive algorithm can be used to predict a number given other numbers?

Do I need classification or regression to predict the availability of a user given some features?

Hot Network Questions

Questions tagged [regression]

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