0
votes
0answers
6 views

Shapley value regression documentation

Can anyone point me to comprehensive description on shapley value regression? I've tried to google it, but i didn't found any book or papers on this regression algorithm, only scraps of disjointed ...
-1
votes
0answers
15 views

How to Build a Foresight System? [migrated]

For a research project, I'm asked to find ways to build an economic foresight system. For example, for the production of cheese. We will have data about the market indicators, like price, demand etc. ...
1
vote
0answers
13 views

Understanding the regularization parameter in polynomial regression

Suppose I have the following $k$ degree polynomial regression model with a data set of size $n$ which includes a $k$-dimensional feature vector $x$ and an outcome denoted $t_i$ for each vector in the ...
1
vote
1answer
35 views

What is the difference between Curve Fitting and Machine Learning?

I know that Machine Learning regression algorithms try to find the function of the data. That is, if we have 1000 data points (x,y), to find a general continuous function that follows the trends of ...
1
vote
1answer
27 views

Combine linear models of different sets of data.

I'm working on a large data set D that can be partitioned into some disjoint subsets D1, D2, ..., Dn. For each subset Di, I have a linear model Mi that minimizes the residual error for data in Di. ...
2
votes
2answers
33 views

Regression model when under-estimations costs us more than over-estimations

We have a factory and we are planning how many items produce in 2014. During the learning process we minimize the mean squared error. But, under-estimations costs us more than over-estimations. Let's ...
1
vote
2answers
45 views

Scaling data into $[-1,1]$

I have a data in the matrix for: \begin{bmatrix} 1 & 2 & 3 & 9 & 6\\ 8 & 2 & 7 & 4 & 6 \\ 1 & 2 & 8 & 7 & 4 \end{bmatrix} Each row corresponds ...
0
votes
1answer
53 views

Is a traditional Multi Layer Perceptron Network capable of non-linear regression? Which activation function should be used for that purpose?

I need to use a Multi Layer Perceptron Network in order to perform some non-linear regression. Any ideas if it's possible to perform a task like that and how? Which activation function should be used ...
0
votes
0answers
126 views

Which machine learning algorithm to use?!

I have a training set which is set of essays written by students for a question. These essays are all scored by human evaluators with labels such as 1, 2 , 3 which is actually marks allotted for those ...
0
votes
1answer
103 views

Show posterior probability takes the form of the logistic function

Suppose you have a D-dimensional data vector $x$ = ($x_1$, ..., $x_n$) and associated class variable $y \in \{0, 1\}$, which is Bernoulli with parameter $\alpha$. Assume the dimensions of $x$ are ...
6
votes
1answer
876 views

derivative of cost function for Logistic Regression

I am going over the lectures on Machine Learning at Coursera. I am struggling with the following. How can the partial derivative of ...
1
vote
1answer
108 views

Machine Learning, why not use matrix multiplication instead of gradient descent?

If we want to minimize our Cost function for a given set of data, why do we use gradient descent and continually guess values until we find a min value for theta when when can just use matrix ...
0
votes
0answers
74 views

Why Local Minimum is calculated for a derivative function instead of actual function?

In Machine learning regression problem, why the local minimum is computed for a derivative function instead of the actual function? Example: http://en.wikipedia.org/wiki/Gradient_descent The ...
1
vote
0answers
45 views

Gaussian prior from feature to input space

if I have Gaussian prior ($\exp\left(\dfrac{-\sum_i w_i}{2\gamma^2}\right)$) on my weights in a linear classifier, how can I transform this so I can apply it for my kernel parameters $\alpha$? I have ...
8
votes
3answers
211 views

Minimize $||Ax-b||$ but for $A$, not $x$

I have a machine learning regression problem. I need to minimize $$ \sum_i||Ax_i-b_i||_2^2 $$ However I am trying to find matrix $A$, not the usual $x$, and I have lots of example data for $x_i$ and ...
0
votes
2answers
166 views

Why Logistic Regression for Classification Problems?

In a class on machine learning, we covered classification problems. In such a problem, you are studying a property of some object, say malignity of tumors in a patient. You are first given a training ...
2
votes
1answer
177 views

An intuitive explanation for neural networks as function approximators ?

We use normal linear regression for modelling functions on datasets . But Can someone explain how neural networks help in approximating more complex ,especially non-linear functions ? intuitively , ...
9
votes
4answers
8k views

regression vs classification

THis is more machine learning questions, but perhaps someone will be able to help. I would like to know what is the diference between regression and classification when we try to generate output for a ...
1
vote
2answers
306 views

Simple Least Squares Regression?

I have a vector X of 50 real numbers and a vector Y of 50 real numbers. I want to model them as y = ax + b How do I determine a and b such that it minimizes the ...
0
votes
1answer
78 views

Regression on Linear Model?

I have 50 or so training examples involving a set of 200 or so real numbers (x1,x2,...,x200) (normalized to a 0 mean and std dev 1), and a single output real (y) in the range 0.0..1.0. I want to fit ...
1
vote
1answer
122 views

can an artificial neural network with only one hidden layer fit all purposes/applications/functions?

I have heard that only a single layer is needed for an ANN to fit any possible function (input to output). Is this true and where is this investigated/state/found? Then what is the advantage of having ...
0
votes
1answer
193 views

dependency between regression coefficients and probability distribution

let's consider regression problem. Given set of training data $\{(x_i,y_i)\}_{i=1}^N$, $x_i \in \mathbb{R}^n$ and $y_i \in \mathbb{R}$, find prediction function $y = f(x)$, e.g. in RBF regression case ...
5
votes
2answers
268 views

Theoretical basis for overfitting

There are many examples in which making more "precise" predictions gives worse performance (e.g. Runge's phenomenon). My professor implied that there was a sound basis for choosing "simple" functions ...