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 ...

learn more… | top users | synonyms

1
vote
0answers
18 views

Finding an equation with many variables to fit a set of data

I am writing a program which takes notes from a keyboard as the input, (just numbers, 1 to 88) and decides which notes are played by which hand. There are a lot of variables, for example, the position ...
2
votes
0answers
15 views

How to subset alternatives in nested multinomial logistic regression?

I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using ...
0
votes
0answers
29 views

How to do multilevel classification using R

I am trying to do a multilevel text classification using R. Is there any package in R which I can use to do it? If not, then how can I proceed using the svm or ...
0
votes
0answers
12 views

Why are Bayesian classifiers “robust to noise”?

In many different settings I've read that Bayesian classifiers like Naïve Bayes and Bayesian Networks are more robust to noise in the input data than other classifiers. I'm wondering what the evidence ...
0
votes
1answer
31 views

Classification Accuracy

I am classifying text based on news headlines and I am achieving accuracy up to approx 80%. I want to improve it more. But issue is that when I calculate the same with synonyms using the code below: ...
0
votes
1answer
23 views

Decision boundary equation of the perceptron

As I know the standard linear equation has the following form in $R^2$: $w_1 x_1 + w_2 x_2 = b$ where $b$ is the intercept with $x_2$ Also I know that a decision boundary in $R^2$ for a perceptron ...
0
votes
0answers
7 views

How to validate classifier (built by using MLN method)?

I have developed a method (let's call it Method X) that has a classifier function. The classifier function was built by using MLN (Markov logic network). I need to ...
2
votes
3answers
52 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 ...
1
vote
0answers
10 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 ...
0
votes
0answers
19 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 ...
0
votes
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
votes
1answer
39 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 ...
0
votes
2answers
24 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 ...
1
vote
1answer
20 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.
1
vote
0answers
31 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 ...
0
votes
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)? ...
4
votes
2answers
200 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 ...
1
vote
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?
0
votes
0answers
9 views

How to Hybrid the clustering and classification model [closed]

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 ...
1
vote
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 ...
0
votes
0answers
20 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 ...
0
votes
2answers
24 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 ...
1
vote
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 ...
0
votes
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 ...
1
vote
1answer
49 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 ...
0
votes
0answers
25 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 ...
1
vote
0answers
25 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 ...
0
votes
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 ...
1
vote
0answers
35 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 ...
0
votes
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 ...
0
votes
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
vote
1answer
31 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 ...
0
votes
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 ...
0
votes
1answer
22 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 ...
0
votes
1answer
24 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 ...
0
votes
1answer
22 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
vote
1answer
34 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 ...
1
vote
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 ...
1
vote
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
vote
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 ...
0
votes
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
votes
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
votes
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 ...
0
votes
0answers
35 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
votes
0answers
32 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
vote
1answer
28 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
votes
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
75 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
vote
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 ...