# Tagged Questions

21 views

### Given a sample of input/output data, predict new outputs

My problem is the following : I have a number of inputs with the corresponding deterministic outputs. There is no error on either input or output. The link between the two is completely unknown to me. ...
62 views

### Why use regularization to reduce over-fitting

I'm having trouble understanding why should we use regularization for over-fitting when we can simply reduce the number of order to our polynomial function? Is it because it saves us time from having ...
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### Deriving cost function using MLE :Why use log function?

I am learning machine learning from Andrew Ng's open-class notes and coursera.org. I am trying to understand how the cost function for the logistic regression is derived. I will start with the cost ...
34 views

### Why divide by $2m$

I'm taking a machine learning course. The professor has a model for linear regression. Where $h_\theta$ is the hypothesis (proposed model. linear regression, in this case), $J(\theta_1)$ is the cost ...
24 views

### How to represent the parameters in logistic function

I want to find the parameters in logistic function. I read the guide at here. It very clear to explain. But it did not has final solution that I need. Now, we will consider a basis logistic function ...
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### How does Linear Regression classification work?

I am currently trying to understand the following: Logistic regression is a probabilistic, linear classifier. It is parametrized by a weight matrix $W$ and a bias vector $b$. Classification is ...
40 views

### How can I plot this?

Given a bunch of data $x_i$ , $y_i$, how do I plot $$f(\theta_2,\theta_2)= \frac{1}{2M} \sum_{i=1}^{M} (\theta_1\cdot x_i -\theta_2 y_i)^2$$ in matlab? I know it should be parabolic, but my code ...
120 views

### Understanding Regularization parameters in Machine Learning/Statistics

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 ...
238 views

### What is the difference between Curve Fitting and Regression(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 ...
35 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. ...
37 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 ...
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### 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 ...
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### 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 ...
135 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 ...
125 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 ...
2k 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 ...
144 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 ...
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### 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 ...
46 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 ...
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### 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 ...
198 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 ...
231 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 , ...
12k 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 ...
366 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 ...
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### 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 ...
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### 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 ...
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 ...