Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a ...

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18 views

Does using a kernel function make the data linearly separable?

I'm reading about SVM and I learned that we use a kernel function so the data become linearly separable in the high dimensional (vector?) space. But then I also learned that they use the soft-margin ...
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8 views

Multi-class semi-supervised classification code (SVM, co-training, Graph-based)?

I am trying to evaluate the performance of my semi-supervised algorithm, by comparing it against different algorithms. I have searched a lot, but can't find code that does multi-class semi-supervised ...
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17 views

Feature scaling for classification and regression

Is it true that one should generally scale each of the features before feeding them into common classification models such as Support Vector Machine, Logistic Regression, etc? What about for ...
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1answer
48 views

I want to learn about ROC curve — what is the canonical textbook?

I want to learn about Receiver-Operator-Characteristic curves, and metrics. I have read through online webpages with some basics, and I have used MATLAB built-ins to create ROC plots. It tells me ...
2
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1answer
33 views

Gradient decay in neural networks

I read that in traditional feed-forward neural nets the gradients in the early layers decay very quickly and that this is 'a bad thing'. But I don't understand why. Can someone please explain what ...
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2answers
21 views

Latent Dirichlet Allocation as input for WEKA

I am using the Weka API for my research about document classification. I wish to apply Latent Dirichelet Allocation on my dataset followed by using a classifier in Weka. However, it is not so clear to ...
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1answer
19 views

Using selected features from a wrapper algorithm to train another model

I was wondering if it can be useful to use selected features from a wrapper algorithm (for example SVM-RFE) to train another classification model like k-NN or Linear regression.
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28 views

Rough estimates for training time of deep belief networks

I'm still learning about deep learning. However I'm currently interested to know if deep learning architectures scale well or not. Suppose I have a dataset with 1 million training examples, can you ...
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0answers
10 views

Why feature maps are indexed by two indices?

I'm reading about convolutional neural networks. As I understood a feature map is a set of neurons (i.e like a single hidden layer in traditional ANN). So why feature maps are indexed by (i,j)? ...
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2answers
188 views

Why is logistic regression a linear classifier?

Since we are using the logistic function to transform a linear combination of the input into a non-linear output, how can logistic regression be considered a linear classifier? Linear regression is ...
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1answer
19 views

Bayes Decision Boundary and classifier

Is it correct to say that the purpose of classifier (e.g. K-NN, Logistic Regression, LDA) is to approximate the Bayes Decision boundary?
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9 views

How to Hybrid the clustering and classification model [on hold]

Hi i am working on classification . By reading some papers on classification i found that the result of hybrid of clustering and classification provides better result. But, i donot know how to hybrid ...
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1answer
33 views

How do we predict rare events?

I am working on developing an insurance risk predictive model. These models are of "rare events" like airline no-show prediction, hardware fault detection, etc. As I prepared my data set, I tried to ...
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0answers
16 views

How to compare the results of two classifiers are statistically significant different?

I am applying kNN and SVM classifiers to my classification problem. Both of the classifiers get over 95% cross-validation accuracy (leave one out cross validation used). Not sure how to tell if the ...
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2answers
19 views

What is the minimum training set size required for a given number of features for document classification?

For document classification problems, is there a rule of thumb for the number of training instances required for the number of terms in the vocabulary? I am using a logistic regression classifier ...
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2answers
23 views

Collecting training data for document classification with unbalanced classes

I have a document classification problem in which the estimated class proportions in the population are severely unbalanced: the population is ~99% class 0 and ~1% class 1. I am using a logistic ...
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0answers
27 views

how to compute odds ratio

Given a generic classification model $y=f(x_1,x_2,..,x_p)$ where $y\in \left\lbrace 0,1 \right\rbrace$ is it possible to compute the odds ratio for each variable? A theoretical explanation and ...
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1answer
46 views

How to define the maximum k of the kNN classifier?

I am trying to use kNN classifier to perform some supervised learning. In order to find the best number of 'k' of kNN, I used cross validation. For example, the following codes load some Matlab ...
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0answers
23 views

Finding best parameters of SVM in matlab

I’m designing a system (using Matlab) that I can optimize parameters of a support vector machine (SVM) with genetic algorithm, harmony search and another optimization algorithms to find the best ...
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0answers
23 views

Outlier detection in binary classification

I have a question about outlier detection in my system. I’m designing a system (in Matlab) that optimize both features and parameters of a classification method (like mlp) together with optimization ...
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0answers
5 views

Precision of an incomplete classifier

Given a testing set of nodes which can be either +,-, or 0, I use an incomplete classifier which allows me to predict if a node is +, -, 0, not +, not -, or not 0, and sometimes it cannot predict ...
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0answers
34 views

A new piece of clue for document classification?

I am working on a document classification problem. I am using the typical vector space model to represent a document as doc-term vector. If document has some term, the vector entry for that term is ...
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0answers
10 views

SVD Down to One Dimension - K=1

I ran an analysis on a very sparse 40K x 40K customer-item rating matrix for recommendations; I first ran SVD on this matrix using many different reduced rank sizes, k=20,30,40... I used the results ...
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0answers
11 views

Complex event processing

I work in an M2M engineering startup and the engineering team have been conceptualizing a complex event processor and want to build "alerts" when an event might occur. The initial plan was to build a ...
1
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1answer
30 views

How to implement a hold-out validation in R

Let's say I'm using the Sonar data and I'd like to make a hold-out validation in R. I partitioned the data using the createFolds ...
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0answers
20 views

Fisher's Exact Test to assess the significance of a difference between false positive rates

I have trained two binary classification models on the same data and evaluated them using the same test set. For each model I have calculated a false positive rate (and a count of false positives) at ...
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1answer
21 views

Should I specify Prior or Cost matrix with Tree Bagger in Matlab

I'm trying to create Random Forests in Matlab and there are more observations in some classes than there are in others. Do I need to specify this as a cost matrix or as a prior probability or will ...
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1answer
23 views

Equal-size categories vs unequal-size categories

I'm trying to reduce the size of my dataset, which is composed of 200,000 projects. Each project is defined by its size and a binary value that is 1 if the project has active users, 0 otherwise. Most ...
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1answer
21 views

ML for specific classification problem

I have a training dataset for classification problem $X \rightarrow y$. Where $X$ is an $n$th dimensional real vector, $y$ is an integer number in $\{0, 1\}$. I want to solve the next problem: ...
1
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1answer
33 views

Why features compression is good?

I'm reading about deep learning and that in principles it's a features compression technique and that is why it works. Now my question is why compressing features from 200 or so into 4 is better? How ...
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2answers
36 views

Can a nuisance multi-class classifier do better than binary classifier?

This is rather a theoretical question in order to save the trouble in trying to do empirical testing and is part of a bet, so I hope I am right... Say there are M classes in the data BUT you want to ...
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0answers
21 views

How to check if a group is similar to another, in two different classifications

Suppose I have two different classification, e.g. C R s1 c1 r2 s2 c1 r2 s3 c3 r2 ... Where sn are the classified samples and the columns of the ...
1
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1answer
36 views

How does the Bayes' theorem equation generalize all sorts of regression/classification models?

I have been reading “Pattern Recognition & Machine Learning” written by Christopher M. Bishop for some time, but I am still a beginner. I wish to get a bigger view that summarizes regression and ...
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0answers
12 views

matlab error with LDA classification [migrated]

I want classify my data with LDA classifier. My test data size is: 1 12 240 64 And my train data size is: ...
0
votes
2answers
43 views

Ensemble of models with different feature spaces

BACKGROUND I have data in which the dependent variable is binary with a highly-skewed distribution: <1% records are 1 (doers), >99% records are 0 (non-doers). I'm using logistic regression to ...
2
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0answers
24 views

Handwriting Recognition - Percentage Match

I'm currently working on a senior project and we've chosen handwriting recognition. Initially I thought that using machine learning algorithms were a good idea for this, but after the thought below ...
0
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1answer
20 views

how to compute minimum required vc dimension for a classifer to classify a specific data

Suppose we're given an N dimensional data to classify. To cope with this task we may choose a classifier that suits our desires more. However obviously not every classifier is capable of classifying ...
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0answers
32 views

Distributions over strings [closed]

I'm trying to preform classification of arbitrary groups of strings. I want to have a feature sort of like string variance (I have a definition of distance between strings already). To do this I would ...
0
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0answers
32 views

HOG Feature Implementation with SVM in MATLAB

I would like to do classification based on HOG Features using SVM. I understand that HOG features is the combination of all the histograms in every cell (i.e. it becomes one aggregate histogram). ...
0
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0answers
29 views

HIstogram of oriented gradients (HOG) features descriptor theoretical problems

I'm going to implement HOG as my features descriptor. But there are some things that make me confused: For example: If we have an image with size of 10 x 20 If we want to compute the HOG of that ...
1
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1answer
24 views

Algorithm for scoring co-varying traits

I am sure this has been done, but I can't find quite the right approach. EDIT: Trying to explain better. The rows of colored boxes below are columns of molecular sequence data -- positions in a ...
0
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1answer
16 views

SVM without offset

I would like to know if the linear-SVM-without-offset solver: $$\min \frac{1}{2}\|w\|^2+C\sum_{i=1}^m \xi_i, \quad \mbox{s.t.}\quad y_iw^\top x_i \geq 1-\xi_i, \quad \xi_i\geq 0 \quad \forall ...
2
votes
1answer
69 views

Relative importance of a set of predictors in a random forests classification in R

I'd like to determine the relative importance of sets of variables toward a randomForest classification model in R. The ...
1
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0answers
12 views

How Does a Disparity in Number of Documents (Training Data Points) Affect Text Classification?

I have collected a fairly clean set of data (5,410 documents) to train a text classifier. I am now attempting to improve my classification success. (Note: When I trained/tested the classifier from ...
2
votes
0answers
59 views

My data lies on a linear plane

My purpose is to classify 3 classes from an EEG data. When I plotted my data on feature space in order to visualize, I found they lie on such a linear plane (please see my figures). Before plotting, I ...
0
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0answers
8 views

Select the most probable learning algorithm from the testing set and output of a classifier?

Let's say I have a "mystery classifier". It is a "black box", I don't know exactly what it is doing, I know neither the training set nor the learning algorithm that was used. What I do have is a ...
0
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0answers
10 views

Training models for classification using different negative datasets

I'm working on a massively unbalanced binary classification task - Classification of given protein sequences as belonging to a certain (very small) class, or not. There are about 1,300 positive ...
0
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0answers
12 views

Classifier accuracy significance assessment in unbalanced train sets

I work with highly unbalanced training sets, and I would like to measure the probability of having certain classifier accuracy by chance in order to measure statistical significances of the accuracies ...
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0answers
22 views

What is the difference between a neural network and Deep neural networks? [duplicate]

Please i m looking for tutorial about neural networks and deep neural networks, -Architecture -Training -etc... Thank you :)
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1answer
37 views

What classification method to use

I have two sets of data from two experiments. All data can be divided in three classes, e.g.: $$\begin{array}{cccc}\rm{Parameter}1&\rm{Parameter}2 ...&\rm{Parameter}N&\rm{Class}\\\hline ...