Questions tagged [machine-learning]
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
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What are the top statistic conferences to follow for applications in machine learning?
What are the top statistic conferences to follow for applications in machine learning?
I just had a discussion, regarding that some of the machine learning jargon and buzzwords are not really sound ...
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How to select kernel for SVM?
When using SVM, we need to select a kernel.
I wonder how to select a kernel. Any criteria on kernel selection?
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How to compare the results of a leave-one-out strategy?
I tried to reproduce the experiments described in this paper and wanted to compare the output of my system with the one described in the article. I am looking for a statistical comparison of the ...
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SVM with unequal group sizes in training data
I am trying to build an SVM from training data where one group is represented more than the other. However, the groups will be equally represented in the eventual test data. Therefore, I'd like to use ...
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Maximum Likelihood Estimation question - minimum log likelihood
I know the formula for the likelihood of some parameters given the data. The result has to be maximised and I can avoid multiplication using the log. How can I make this a minimisation problem (i.e. ...
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Cloud computing platforms for machine learning [closed]
I've got a small list of companies that provide a platform for running R, python, or octave scripts on clusters built on top of amazon EC2. Are there other names I should add?
Cloudnumbers
Opani
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How does normalization reduce dimensionality of data?
While reading a SVM tutorial, the author makes the following statement on normalization technique for processing the input data:
Normalizing data to unit vectors reduces the dimensionality of the ...
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Correct way of testing machine learning against random data
I am using a genetic algorithm to search a very complex hypothesis space. Now I want to estimate how much overfitting I can expect in the final resulting hypothesis. The final model will be used for ...
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Consistency of the learning process
I have two questions related to the concept of "learning consistency" for those who are familiar with statistical learning theory a la Vapnik.
Question 1.
The learning process is called consistent (...
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Why would concatenating feature vectors lead to better estimates?
I wish to estimate the state of a system from two separate and disparate observations. A simple approach that I have seen in some literature is to combine the feature vectors (observations) by simply ...
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Why does ridge regression classifier work quite well for text classification?
During an experiment for text classification, I found ridge classifier generating results that constantly top the tests among those classifiers that are more commonly mentioned and applied for text ...
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GDA and LDA terminology
Can the terms LDA (Linear Discriminant Analysis) and GDA (Gaussian Discriminant Analysis) be used interchangeably?
Do they often refer to the same thing?
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Two-way unsupervised learning
I have two data sets, A and B. Both have a large number of continuous variables. I believe that A is related to B. But there are no defined classes in either A or B. I could do a bunch of correlation ...
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How do you calculate variable importance p-values using the randomForest package in R?
For a classification project we are using the randomForest package in R, which wraps the Breiman Fortran random forest implementation, to assess the importance of each of our features. I would like to ...
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Detecting changes in distribution of multiple variables
I am a bit new to this field. So I needed help in finding out which topic should I focus on for achieving this.
Suppose I have N dependent random variables. I have n samples of each of these random ...
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Logistic regression with LBFGS solver
Is there any open source library or code which implements Logistic Regression using L-BFGS solver?
I would prefer Python, but other languages are welcome, too.
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Data prep / variable creation for predictive models
I was reading a couple of the write ups from a Kaggle challenge: Here is one and another
and it got me wondering about variable creation in data mining and why there seems to exist so few texts or ...
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What are the "hot algorithms" for machine learning?
This is a naive question from someone starting to learn machine learning. I'm reading these days the book "Machine Learning: An algorithmic perspective" from Marsland. I find it useful as an ...
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When over/under-sampling unbalanced classes, does maximizing accuracy differ from minimizing misclassification costs?
First of all, I would like to describe some common layouts that Data Mining books use explaining how to deal with Unbalanced Datasets. Usually the main section is named Unbalanced Datasets and they ...
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Category selection for text classification
It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
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What is a good resource that includes a comparison of the pros and cons of different classifiers?
What is the best out-of-the-box 2-class classifier? Yes, I guess that's the million dollar question, and yes, I'm aware of the no free lunch theorem, and I've also read the previous questions:
What ...
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Statistical analysis on categories before text classification
I want to classify text by different topics.
However, one of the current problems is that there are several topics/categories that are quite intuitively independent and statistically standalone, but ...
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Accuracy of a random classifier
I was wondering how to compare accuracy of my classifier to a random one.
I'm going to elaborate further. Let's say we have a binary classification problem. We have $n^+$ positive examples and $n^-$ ...
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How to get started in data-relation algorithms and mathematics?
I am very interested in the concepts and discussions taking place here. However, I'm not entirely sure what this field of study is called or what the many branches of study are being discussed here ...
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Which kernel function for Watson Nadaraya classifier?
I am trying to implement a Watson Nadaraya classifier. There is one thing I didn't understand from the equation:
$${F}(x)=\frac{\sum_{i=1}^n K_h(x-X_i) Y_i}{\sum_{i=1}^nK_h(x-X_i)}$$
What should I ...
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GMM with Bayes decision model
Given two classes of training data (A and B), I want to fit each class' distribution using a GMM with k components, and then use Bayes Decision Model for the classification.
The first step was to ...
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Semi-supervised learning, active learning and deep learning for classification
Final edit with all resources updated:
For a project, I am applying machine learning algorithms for classification.
Challenge:
Quite limited labeled data and much more unlabeled data.
Goals:
Apply ...
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Benefits on kernel trick and one related question
My questions are:
What are the benefits of kernel trick? Can anyone summarize?
One thing I read about in one lecture note is , 'we never need to explicitly represent feature vectors'.
I do not quite ...
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What does interaction depth mean in GBM?
I had a question on the interaction depth parameter in gbm in R. This may be a noob question, for which I apologize, but how does the parameter, which I believe denotes the number of terminal nodes in ...
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How to quickly select important variables from a very large dataset?
I have a dataset with about 2,000 binary variables/200,000 rows and I'm trying to predict a single binary dependent variable. My chief goal at this stage isn't getting accuracy of prediction, but ...
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Different optimal number of boosting iterations obtained from OOB and on test
If I'm using a machine learning model (e.g. boosted regression trees like gbm in R) on a dataset, what does it mean if there's a significant difference between the OOB estimated optimal # of ...
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Getting critics to recognize that two similar input patterns refer to the same output-performance relationship
The actor-critic model is used within temporal difference learning, which is a method within reinforcement learning, to optimize a process on a state-by-state basis by using the difference between ...
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Machine learning in web application?
There are a lot of outstanding machine learning/data mining standalone applications available in different languages like Java, Python, and others.
However, I wonder, practically, in case of applying ...
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Factors that affect variation in the data?
What is the practical way to identify the factors that create variation in a data of a dataset? What category does this question fall into? Are there a set of algorithms that can be used for this ...
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Paired multiarm bandit
I have a set of independent experiments with different distributions and I'm trying to determine which has the highest mean payoff. I would like to treat this as a multi-arm bandit problem, but the ...
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Metric for probability based classification
I am doing a system for classifiying documents. The project demands the use of probability based output. So a sample will have a probability for belonging to each class.
For now I use logistic ...
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How can I implement multiclass k-NN?
I want to implement k-NN to use in a multi-class dataset. I found "A k-Nearest Neighbor Based Algorithm for Multi-label Classification" but didn't get the algorithm.
Do you know any clear explanation ...
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Best practices for measuring and avoiding overfitting?
I am developing automated trading systems for the stock market. The big challenge has been overfitting. Can your recommend some resources describing methods for measuring and avoiding overfitting?
I ...
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Validation of clustering results
I have a data which contains several columns which I later reduced using a PCA algorithms to two different components. I then applied the k-means algorithms to the data.
Now, how can I verify that my ...
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Predictive model for network data
Assume a network as a set of data, which are defined by their coordination $(x,y,z)$ and a weight on its edge. Now this data can be used as an input data to predict a single value.
In my case, ...
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Automatic text quality grading
I came up with an idea to use machine learning for automatic grading of topic-specific texts.
More specifically, I will first use normal text classification techniques to sort all candidate texts ...
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How to characterize a problem of standardizing product descriptions [closed]
I'm looking for some advice for where to start on this problem. Let's say I have sales transaction data from a number of different retailers that all sell the same products. Even though they are ...
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Machine learning task with feedback loop
What are the available options if I want to perform a scoring task on a set of observations that: a) have a set of variables connected to them and, b) each round I get new information about the ...
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In natural language parsing, what is the feature function?
I'm working in the area of natural language processing, to be specific I'm reviewing the parsers that take advantage of data-mining techniques.
I've read an introduction to natural language ...
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Text categorization/classification for small scale text
I'm looking into a way to classify/categorize sentences into pre-defined categories (around 10-15). Yes, indeed sentences, not articles or paragraphs.
Given the average length of articles are not too ...
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Using a histogram to estimate class label densities in a tree learner
In a sequential (on-line) tree algorithm, I'm trying to estimate class label densities using a histogram. The algorithm grows a tree by generating test functions at each decision node, which ...
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How can it be proved that if number of samples are less than $d+1$, then the sample set is linearly separable?
Pattern classification by Duda, Hart, Stork (Section- 9.6.8) states that a 2-class training set of $d+1$ or less samples in a d−dimensional space is always linearly separable i.e. if the samples span ...
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Suggest an exhaustive procedure that will find a separating vector for linearly separable pattern in a finite number of steps
I read in a pattern classification text, that if we consider weight vectors whose components are integer valued, the perceptron procedure would terminate in a finite number of steps.
What is the ...
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Interpreting distance from hyperplane in SVM
I have a few doubts in understanding SVMs intuitively. Assume we have trained a SVM model for classification using some standard tool like SVMLight or LibSVM.
When we use this model for prediction ...
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