Methods and principles of building "computer systems that automatically improve with experience."

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how to superimpose two matlab images rigidly transformed to perform a metrics

i got two MRI images on matlab, i need to perform an intensity based registration similarity metrics in order to get a registration. The problem is that, since i got some rigid transformation on one ...
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1answer
17 views

Weight Decay in Neural Neural Networks Weight Update and Convergence

I have a neural network (That I created using java) for a class assignment that is working when I do not use any weight decay value, but when I use a value greater than or equal to .001, my accuracy ...
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35 views

Using third validation set in Cross Validation?

(Note there's 2 paragraphs of background information before I get to the question) I've got a Neural Network classifier, trained with an EA to classify data. I previously used a holdout framework ...
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1answer
20 views

Logistic-Regression: Prior correction at test time

Using sklean.linear_model.LogisticRegression for a binary classification problem. My classes are unbalanced. The positive class comprises about 20% of the training set. When fitting the model I use: ...
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1answer
35 views

Crossover and Mutation in Genetic Algorithm

I am studying how GA works. It is known that GA obtains the optimal solution by iteration through the the process of reproduction, crossover, and mutation. When crossover and mutation, the ...
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3answers
113 views

Understanding Gaussian Basis function parameters to be used in linear regression

I'd like to apply the Gaussian basis function into a linear regression implementation. Unfortunately I'm having a hard time understanding a couple parameters in the basis function. Specifically mu ...
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29 views

Machine Learning Models For Real Time Sales Data

I am working on a predictive analytics problem related to Sales where based on interaction with a prospect we try to predict whether the deal will close or not. In sales, the data updates whenever a ...
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194 views

In Graph Transformer Networks, which parameters are tuned during back-propagation?

Referring to the milestone paper "Gradient-Based Learning Applied to Document Recognition" of LeCun, Bottou, Bengio and Haffner, which parameters of the graph transformer networks for global training ...
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27 views

A simple question on CLT in possible connection with Berry-Esseen thm

I am curious about the contents while I read a note on machine learning. It could be obvious. So, please let me know if I am missing some fundamental things. $X_1,X_2,...,X_n$ are from an i.i.d. ...
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27 views

Neural Networks sigmoid activation with bias updates

I am trying to figure out if I am creating an artificial neural network using the sigmoid activation function and using bias correctly. I want one bias node to input to all hidden nodes with static ...
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2answers
183 views

Pattern recognition with time series analysis

I'm looking for some good pointers to pattern recognition with time series. Possibly something practical that can be easily understood. As a toy example, think about collecting data from an ...
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1answer
27 views

Use of infinity norm instead of SSE for machine learning accuracy?

Are there any examples or arguments in favor of using an infinity norm (or equivalent) over sum of squared errors or root mean squared error for evaluating machine learning algorithms?
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11 views

What is the relationship between separable SVM test error bounds and soft-margin SVM test error bounds?

Can I compute the bounds for a soft margin SVM by taking the VC dimension for an SVM and using the misclassified examples as train error? Does the inequality from wikipedia's VC dimension page hold ...
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72 views

Strategy for Analyzing Data

I have been learning about Machine Learning (via Udacity) and Statistics (via Coursera) the past few months and trying to figure out a good way to combine them for a general approach to explaining ...
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32 views

What is the next step after acquiring the parameters(means, covar, priors) from GMM via EM

I am comparing the results achieved from clustering via K-means and GMM. For comparison I have accumulated a dataset of images. The training set consists of 359 images. I used SIFT to extract the ...
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28 views

How to calculate multicollinearity of binary variable with other predictors in regression model?

VIF can be used to calculate multicollinearity of continuous variable in regression models. But VIF will only work for continuous variables because this is calculated by running a linear regression ...
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45 views

Dichotomizing Continuous Variables in Regression: Good or Bad? [duplicate]

I believe Dichotomizing(also called bucketing/binning) of continuous variable is not always a good idea. My colleague while building regression model always bins continuous variables and only keep ...
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21 views

Intuition on One Class Support Vector Machines

I am having trouble understanding how one-class SVMs work. They were introduced in a paper by Scholkopf and others (and can be found here). One-class SVMs perform "novelty detection", where a point ...
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41 views

What is a convolutional neural network

I have been studying neural networks and I recently found out about deep learning and convolutional neural networks. Can someone give me a newbie introduction to convolutional neural networks, what ...
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1answer
18 views

Affinity propagation comments

I was looking into affinity propagation for my similarity matrix problem and thought it would fit well. However, browsing literature I found this comment that basically breaks both legs of affinity ...
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36 views

Classes distribution in training set

In my actual data class A has 90%, class B has 9% and class C has 1% (numbers are made up for sake of simplicity). Now I want to prepare a training set for my classifier (I plan to use Vowpal Wabbit). ...
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8 views

Use cases for P-Kernel for SVMs

I've been reading the book by Cristianini on Kernels (2004) where generative kernels (like p-kernel and fisher-kernel, not to be confused with polynomial kernel!) are described. I am interested in ...
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20 views

Comparison of supervised learning algorithms with different data types

I've been looking for review type papers of supervised learning techniques that focus on the type of data being used to train e.g. factors with many levels, binary factors, continuous variables etc. I ...
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2answers
21 views

What's the best algorithm type for low-dimensional grouping

I'm looking for some advice on directions to head in a project I'm working on. Basically what I want to do is identify general (of varying size) groups in a 2-D grid of points belonging to one of ...
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2answers
54 views

Understanding differences between large and small dimensional data when implementing algorithms

I'm working on a local outlier factor implementation based on the wikipedia entry : http://en.wikipedia.org/wiki/Local_outlier_factor This article seems to explain it in just two dimensional data. ...
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1answer
24 views

Question of unary term (data term) of the graph cut method

I am trying to apply graph cut method for my segmentation task. I found some example codes at Graph_Cut_Demo. Part of the codes are showing below ...
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37 views

predicting time series with support vector machine using R

I am planning to do time series prediction using support vector Machine. I could not find any materials about time series application of support vector machines using R or Mat-lab. Similar question ...
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38 views

In a neural network of n features which predict a continuous variable X, how to tell the feature which contributes the most to the output value X?

Say I have a neural network which uses some input features say N, some inut layers say L which predict a continous variable say X. Can we say which features or combination of 2 features of the initial ...
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50 views

Varying LIBSVM predictions based on test series labels

So I have a pretty well testing SVC train series which puts me into the mid 80 percentile without outrageous C/g values. My current C value is 2.0 and gamma is 0.5. Good numbers across the range ...
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42 views

How to prepare my data for SVM classifier in matlab

I am new to SVM and Matlab. I would like to have an example how to prepare my data to be as input to the SVM classifer (using libsvm) let us assume that i have a group of words first i have ...
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19 views

Combined Two CLassifiers

I am involved in a research where i need to classify group of words (strings) into two classes I am currently reached a dead point where my classifier is not doing as i expected. I used like three of ...
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47 views

Random variables of mixed models

I am thinking about using mixed models as part of my research, but I am having trouble understanding its application. In particular, I have two somewhat related questions regarding mixed models. ...
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1answer
12 views

Can adaboost choose the same variable for multiple splits for a given tree?

Can adaboost choose the same variable for multiple splits for a given tree? The model was given 100 + variable to choose from and it did choose them for the other trees in the ensemble. I am using ...
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12 views

Naive based model for positive negative classes

I am new to the concept of machine learning and I am trying to figure out this problem. I have 4 clusters who's means kind of form a square. The training dataset that I provide to Naive Bayes has 2 ...
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21 views

Live selection of movie to suggest based on similarity of users

I am working with movie selection for users. 1 ) One of the first ways I thought was taking all the clicked only movie data and building decision trees out of it. Then when input is passed, the ...
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14 views

get a list of predicted variables based on rank

I have a data of device, location, age etc of some users, along with keywords that they clicked (or not). Based on this I want to build a model that predicts a list of keywords for test set that ...
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1answer
26 views

Overfitting with low test error

I have an ensemble of models that yields overfitting by some models by looking at the differentce between training and test error and also overfits in the combined ensemble of models providing a very ...
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26 views

Extracting fixed-length feature vectors from variable-length time series

I have a classification problem where I would like to develop a binary classifier to classify between two different types of objects, given a time-series (signal) related to that object. The problem ...
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1answer
20 views

Requirements for a valid neural network activation function?

What rules define a valid neural network activation function, excluding biological plausibility? What set of principles do softmax, rectified linear units, hyperbolic tangent, sigmoid, etc. follow? ...
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1answer
45 views

How to find all optima in an optimization problem?

I have an optimization problem where several optima can exist at different input values, and I need to find as many as possible. As an example consider the cross-in-tray function, which has four ...
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49 views

What enforces features diversity in RBM?

I'm working on an implementation of a Restricted Boltzman Machine (RBM). I made some tests on the MNIST dataset trying to learn a representation of the digit 2. My inputs are binary images. My aim is ...
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1answer
116 views

Suggestions needed about classifier fusion

I'm working on a classification problem which involves two classifier to observe a single event. I'm providing a high level description of the problem without going into the technical details (the ...
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9 views

Partially sparse vectors for training classifiers

Is it a bad idea to use a partially sparse vector for training a logistic regression classifier? By "partially sparse", I mean that about half the vector is actually dense, with real valued numbers ...
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1answer
57 views

Machine learning - curve categorisation

I have curves of the following structure (it is the blue one I am interested in) These curves reflect the volume of blood (actually gamma ray counts) in the left ventricle as a function of time ...
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1answer
30 views

Statistical Commute Analysis in Java

I have a rather large commute every day - it ranges between about an hour and about an hour and half of driving. I have been tracking my driving times, and want to continue to do so. I am capturing ...
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17 views

Feature/Variable selection to accompany mixed models?

I am trying to conduct an exploratory/data mining analysis to discover what socioeconomic factors best predict grade-school performance in children. I have a dataset with about 50000 ...
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3answers
125 views

Optimal classification model for translating words

I have the following problem: I have a set of English words which I want to translate to Dutch. Of each words I mined a set of possible translations. For example, for the word "Eighteen" I obtained ...
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1answer
48 views

Algorithm to find subsets with high correlation

I have a reasonably large dataset (d) with predictor variables x1...xn and a target variable y. I can use recursive partitioning (such as CART or rpart in R) to find subsets of d with a high (or low) ...
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8 views

Is it safe to mix different encodings of features in one data set

I'm investigating which feature encoding yields the best results for training a predictive model on a biological problem. All variables have the same format and possible values before conversion to ...
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24 views

VC-Dimension of n-node binary decision tree in N-dimension feature space

Given input feature space $\mathcal{X} =\{0, 1\}^N$ and output label space $\mathcal{Y}=\{0,1\}$ , prove that the VC-dimension of a binary decision tree with $n$ nodes is in $O(n\text{log}N)$. I've ...