Tagged Questions

2
votes
2answers
59 views

Why can the margin of SVM be approximated by 1?

The separate function of SVM is : $wx+b=0$ The function distance of support vector to the separate plane is : $|r| = wx_i+b$ And we can normalize the $w$, then the distance can be write as : ...
2
votes
0answers
36 views

SVM classifier (with soft-margin) implementation in R, gamma value and quadprog

I'm trying to implement a Support Vector Machine classifier in R and I have to solve the optimization problem using the quadprog R package which solves problems of the form : $$min_b \frac{1}{2} ...
0
votes
1answer
44 views

regularized logistic regression and support vector machine

L2 regularized logistic regression differs with L2 regularized support vector machine with their loss function. Are there more deep differences for these two models? I tried several data sets, and ...
1
vote
0answers
37 views

Which Regression methods are suitable for binary valued features and continuous output?

I want to build a machine learning model to regression on continuous output given binary valued features(0,1). the dimension of my problem is around 200. which of the flowing methods seems suitable ...
1
vote
1answer
23 views

Bias term in support vector machine

In SVM, there is a bias term. But looks to me there are very few discussions on the physical meanings of this term. Why should we have that? How does this term affect the model?
1
vote
1answer
78 views

When does Naive Bayes perform better than SVM?

In a small text classification problem I was looking at, Naive Bayes has been exhibiting a performance similar to or greater than an SVM and I was very confused. I was wondering what factors decide ...
1
vote
1answer
30 views

Evaluating features and similarity measures

I am currently developing a classificator, which is supposed to classify into a number of classes. For this purpose I am designing some features and similarity measures which I might use for a later ...
0
votes
0answers
30 views

Predicting with Relevance Vector Machines

I am trying out this Matlab toolbox for Relevance Vector Machines by Tipping: http://www.miketipping.com/sparsebayes.htm This has an implementation of Relevance Vector Machines, and generates pretty ...
1
vote
3answers
61 views

Order of Support Vectors, and how to reduce them

I am working in an extremely memory constrained environment, and the number of support vectors my Matlab design is generating is just not something that scales. That led me to move to finding a way to ...
2
votes
1answer
54 views

What exactly is the equation for SVM classification for new example?

I understand that in the case of Logistic Regression, we simply multiply our weights with Input example for classification. But what exactly is the equation that we calculate in the case of SVM to ...
2
votes
3answers
178 views

Why is svm not so good as decision tree on the same data?

I am new to machine learning and try to use scikit-learn(sklearn) to deal with a classification problem. Both DecisionTree and SVM can train a classifier for this problem. I use ...
1
vote
2answers
57 views

Highly unbalanced test data set and balanced training data in classification

I have a training set with about 3000 positive instances and 3000 negative instances. But my test data set is pretty much un-balanced. The positive set only has 50 instances and negative has 1500 ...
0
votes
0answers
42 views

Energy estimation through machine learning

Greedings to everybody. I have the dataset which you can find here, containing many different characteristics of different houses, including their types of heating, or the number of adults and ...
0
votes
1answer
17 views

in nonlinear binary classification problems, which is the optimal dimension for make it lineary separable?

My question pertains to linear separability with hyperplanes in a support vector machine. Is posible to determinate the optimal dimension in which i have to transform a training data set for make it ...
-1
votes
0answers
23 views

kernels distances gram matrix classification

Could you please explain some thing about kernels? As I understand it is technique to map the feature space into a high dimensional feature space where we could separate two classes by a linear ...
0
votes
0answers
26 views

Confusion related to L2 and L1 SVM

I have this confusion related to L1 and L2 svm. I was reading this paper I am attaching the screenshot and the part I didn't understand The part that I didn't understand how it was derived I ...
0
votes
0answers
25 views

coordinated dual descent method and sequential minimal optimization

Libsvm uses the sequential minimal optimization as its main solver while Liblinear uses coordinated dual descent method. What are the major differences between these two methods? Looks like both of ...
1
vote
0answers
52 views

Increasing the value of C in SVM (LibSVM) is not changing the accuracy at all

I am trying to learn SVM Classifier using some amount of training data. and then I am predicting for another set (independent from training data) I tried random C values from 0.000001 to 50000000 and ...
2
votes
2answers
80 views

ML with fastest classification speed

I have a data classification problem and I'm wondering what is the best machine learning approach to use for the particular constraints of my problem. My constraints are as follows: - the data ...
7
votes
1answer
271 views

Occam's razor obsolete?

I saw Vapnik's books about statistical learning... I read the first few chapters. Anyway what surprised me the most was that he thought that the Occam's razor was obsolete. I thought it was related ...
0
votes
0answers
51 views

Normalizing SVM predications to [0,1]

I have trained an linear SVM which takes a pair of objects, computes features and is expected to learn a semantic similarity function between objects(we can say that it predicts whether the two ...
0
votes
0answers
40 views

Any basic Wavelet kernel implementation for SVM?

I have looked at at the toolbox by Alain from here http://asi.insa-rouen.fr/enseignants/~arakoto/toolbox/index.html but it comes with bad documentation. Does anyone have experience here implementing a ...
0
votes
0answers
45 views

Classifying high-dimensional data

I'm only learning about classification but why is it common practice to use PCA before using a Support Vector Machine? Assuming I have 128*10 features and only 90 datapoints for each, do I need to ...
0
votes
1answer
123 views

understanding of libsvm output

I applied libsvm to build a text classifier. The output looks like as follows: ...
0
votes
0answers
81 views

How to compute precision for a multiclass problem?

I have a question about calculating precision on a multiclass problem. If the true positives of some actual class is 0, and its false negatives is also 0, then how to calculate its recall? In this ...
2
votes
1answer
123 views

Using the appropriate machine learning algorithm

I am not sure if this is the right forum to ask this. I have some data of the houses, like their size(in square meters), if they use aircondition, how many residents live in, I have their electricity ...
0
votes
0answers
43 views

How to solve a dilemma case on Support Vector Machine?

I am learning the SVM classification and especially interested in applying to medical data. Now, I encounter a problem and do not know if this dilemma has a terminology for it. Assume that there ...
0
votes
1answer
53 views

liblinear one vs rest learn parameters

Liblinear (http://www.csie.ntu.edu.tw/~cjlin/liblinear/) does not support for probability estimates. Say I have three classes C1, C2 and C3. I want to learn the model paramters for each 'one vs rest' ...
4
votes
2answers
534 views

how to calculate precision and recall for multiclass classification using confusion matrix?

all, I wonder how to compute the precision and recall using confusion matrix for multi-class classification problem. In specific, one data can only be assigned with most probable class/label. I like ...
0
votes
0answers
56 views

LIBSVM-based classifier assign very low score to positive validation files

Recently, I have been applying the LIBSVM to build a classifier based on a set of documents. The positive set has about 20000 files and negative set has about 50000 files. The built classifier is then ...
0
votes
0answers
51 views

the effects of feature matrix format on the training time of LIBSVM

I am using Libsvm to perform text classification tasks. I normally uses binary occurrence, TF/IDF to build feature set for the input documents. It normally takes quite longer for Libsvm to finish ...
1
vote
0answers
113 views

SVM failing entirely with when test set is varied

I am experiencing a strange problem with varying the test set size. This is mildly confusing to explain, but I'll do my best. I'm using octave to train an SVM on timeseries-like data and it's ...
2
votes
0answers
139 views

Bias items and probability estimates in LibSVM

I have two questions in using LIBSVM The decision function for C-support vector classification is $$\text{sgn}\left(w^T\phi(x)+b\right)=\text{sgn}\left(\sum_{i=1}^ly_i\alpha_iK(x_i,x)+b\right)$$ ...
5
votes
1answer
183 views

What are the advantages of Multiple Kernel Learning (MKL) methods?

Multiple Kernel Learning methods aim to construct a kernel model where the kernel is a linear combination of fixed base kernels. Learning the kernel then consists of learning the weighting ...
2
votes
2answers
109 views

Increasing the sample size does not help the classification performance

I am training a SVM classifier based on a given document collections. I started from using 500 documents for training, then I add another 500 for training, and so on. In other words, I have three ...
1
vote
1answer
66 views

Relationship between number of training set and classification performance

Are there any research/paper on the relationship between the number of documents for training and the classification performance using support vector machine?
2
votes
2answers
127 views

Where does the definition of the hyperplane in a simple SVM come from?

I'm trying to figure out support vector machines using this resource. On page 2 it is stated that for linearly separable data the SVM problem is to select a hyperplane such that $\vec{x}_i\vec{w} + b ...
0
votes
0answers
57 views

What are hidden Markov support vector machines?

What are hidden Markov support vector machines, and how do they compare/relate to HMMs and SVMs?
0
votes
0answers
64 views

How many parameters does a HM-SVM require?

How many parameters does a Hidden Markov Support Vector Machine require?
2
votes
0answers
99 views

Kernel SVM in primal training with Stochastic Gradient Descent

In short: I am currently reading Online Learning with Kernels (http://books.nips.cc/papers/files/nips14/AA33.pdf) for fun and I can't figure out how he got to equation 8 from equations 6 and 7. The ...
2
votes
1answer
199 views

What is “feature space”?

What is the definition of "feature space"? For example, When reading about SVMs, I read about "mapping to feature space". When reading about CART, I read about "partitioning to feature space". I ...
1
vote
1answer
73 views

SVM optimization problem

I think I understand the main idea in support vector machines. Let us assume that we have two linear separable classes and want to apply SVMs. What SVM is doing is that it searches a hyperplane ...
3
votes
2answers
233 views

SVM prediction sensitivity when compared to neural networks and logistic regression

Basically I want to classify a rather rare status (about 2% of the 2000) with some predictors. I have used logistic regression, neural network, and Support Vector Machines to do it. All the ...
3
votes
1answer
85 views

Regarding redundant training data in building SVM-based classifier

To build a SVM-based classifier, I have a training data set consisting of N data points. Some of them are redundant. For instance, there have 50 data points which are exactly the same, and there have ...
3
votes
1answer
103 views

Prediction using machine learning

Say I have some data for past 5 years and I have trained my classifier (anything decision tree, svm etc.) based on that i.e. given the appropriate input feature data and correct output labeling. Now ...
4
votes
2answers
121 views

When there are many more failures than successes should I let classes be equal in SVM?

I have about 5544 runs where I am trying to classify it as failure or success. Here the number of runs that lead to failure is only 64 and rest is sucess. In that case when I try to use SVM should I ...
3
votes
2answers
252 views

training approaches for highly-imbalanced data set

I have a highly-imbalanced test data set. The positive set consists of 100 cases while the negative set consists of 1500 cases. On the training side, I have a larger candidate pool: the positive ...
0
votes
1answer
91 views

Regarding the feature generation method with SVM-based classification method

When using SVM to build classifier for a collection of documents, we can use term occurrence, term frequency or even TF/IDF. I would like to know what are the main disadvantages of using term ...
1
vote
1answer
107 views

The general approaches for improving a SVM-based classifier which is low precision and high recall

I built a SVM-based classifier against a data set, the precision is about 66% and the recall is about 88%. Generally, what are the options to tune the parameter that can increase the precision?
1
vote
1answer
166 views

Different prediction score for two SVM-based classifiers

As a validation study, I use two libsvm-based svm classifier against the same data set. One classifier is libsvm implementation in Rapidminer. Another classifier is Libsvm itself. Both of them assume ...

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