Tagged Questions

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

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0
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0answers
18 views

PLA vs Regression

Just started getting into machine-learning, and I'm wondering if there is a relationship between the Perceptron learning algorithm and linear regression?
3
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1answer
35 views

prequential evaluation - classification

I perform prequential evaluation like this: start with a training set, classify a number of examples, then add the correctly classified examples in the training set and continue to classifying the ...
0
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0answers
26 views

Scikit-learn reports memory error when fitting Gaussian process model

I want to fit a Gaussian Process with about 50,000 training examples and 130 features using Scikit-learn. Right now, I only have 1 theta hyperparameters as I run the process with all defaults. But I ...
1
vote
1answer
28 views

Categorical value “stuck” during sampling of my model

I'm having some troubles with the implementation in pyMC of my probabilistic model. Note: you can skip directly to the code section, if you're not interested on the use of the model. The model ...
1
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0answers
23 views

The weight update in adaboost

1.AdaBoost updates the weight of the sample By the current weak classifier in training each stage. Why doesn't it use the all of the previous weak classifiers to update the weight. 2.It need to ...
0
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0answers
17 views

Estimating sparse inverse covariance matrix in high dimensional data

I am trying to estimate the graph in very high dimensional data, I mean with million nodes. Up to now all the papers that I have found, they are limited to few thousands. All of them like graphical ...
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2answers
30 views

clustering gene expression data

I have a question about clustering. I' m managing gene expression microarray data and I would like to cluster them in classes. I searched around to find the best clustering algorithm for my data, ...
-1
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0answers
67 views

Is it fair to say that time-series violates IID? [closed]

First a little background. It is said that, for the supervised learning framework. there should be a probability distribution over the input space, $P$ over $X$, (for example, as stated here ...
4
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3answers
107 views

Should you ever standardise binary variables?

I have a data set with a set of features. Some of them are binary (1=active or fired, 0= inactive or dormant) and the rest are real valued, i.e. 4564.342. I want to feed this data to a machine ...
0
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1answer
58 views

Minimize a function with respect to a matrix

I have two sets of vectors, A and B. Vectors from set A live in an m-dimensional space, ...
2
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2answers
77 views

What machine learning techniques can, once trained, generate prediction despite some missing inputs?

I have a training set where the inputs & outputs are all present, but I suspect that in the data where I want to do prediction, I will occasionally encounter scenarios where a small fraction of ...
2
votes
2answers
63 views

Can machine learning methods be somehow helpful in solving differential equations?

I noted that regression task in machine learning is somehow related to solving differential equations approximately - both are trying to approximate unknown function. Then, my question is: Can ML be ...
2
votes
1answer
85 views

Next steps after “Bayesian Reasoning and Machine Learning”

I'm currently going through "Bayesian Reasoning and Machine Learning" by David Barber and it is an extremely well written and engaging book for learning the fundamentals. So a question to someone who ...
1
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1answer
50 views

Online logistic regression?

Here is my problem: I am developing an embedded system for some classification task. I am using Logistic Regression as my classifier. Now I train my classifier, and download my model on to my machine. ...
0
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0answers
21 views

time complexity and space complexity for HMM forward recursion

When Reading the HMM models, I found the following discussion on the time complexity and space complexity regarding forward recursion. I am sort of confusing on the reason of getting O(K^2N) and ...
0
votes
1answer
68 views

First steps learning to predict financial timeseries using machine learning

I am trying to get a grasp on how to use machine learning to predict financial timeseries 1 or more steps into the future. I have a financial timeseries with some descriptive data and I would like to ...
0
votes
1answer
46 views

How to adjust machine learning training data set with time

I'm using machine learning to do text classification right now, I first use a training data to train my classifier, then use this classifier to classify text document into different classes. With the ...
0
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0answers
35 views

Appropriate method for supervised learning of small data set with few variables

What method exc. for regression can be used in order to get y=f(x1,x2) on a training set of 800 to 2000 samples? y is a whole number <0,15>, x1,x2 are real <0,40>? I'm interested in prediction ...
0
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0answers
21 views

Strategy for building best fit multiple regression model with time lagged variables

I am building a multiple regression model - wrapped in a function - with one dependent variable and a dozen independent variables. The reason why I am building a function is that I need to do this ...
0
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0answers
18 views

Adaboost with a basic linear classifier

I am trying to implement the adaboost algorithm with the simple linear classifier as weak learner. For this I am using the pseudo inverse rule. i.e. w = inv(X*X')*X*t, where w is the weight vector of ...
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0answers
24 views

Find exceptional parameters

I have been given an excel document with many rows full of numbers, some rows are marked. Each row represents a case in the clinic, each column represents a research test parameter. I need to find, ...
2
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2answers
66 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
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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} ...
2
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0answers
69 views

Universal Approximation Theorem — Neural Networks

I have posted this question elsewhere--MSE-Meta, MSE, TCS, MetaOptimize. Previously, no one had given a solution. But now, here is a really excellent and comprehensive answer. Universal approximation ...
2
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1answer
46 views

Compare classifiers based on AUROC or accuracy?

I have a binary classification problem and I experiment different classifiers on it: I want to compare the classifiers. which one is a better measure AUC or accuracy? And why? ...
-1
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0answers
33 views

Performance and Parallelization of Dimensionality Reduction Algorithms [closed]

I'm trying to implement a (nonlinear) dimensionality reduction algorithm (and I am new to the field). Now, my question is : How much can I boost the performance of these algorithms (a list of ...
2
votes
1answer
42 views

Polynomial regression using scikit-learn

I am trying to use scikit-learn for polynomial regression. From what I read polynomial regression is a special case of linear regression. I was hopping that maybe one of scikit's generalized linear ...
0
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0answers
19 views

How to do warm start with validation?

I have a cost function that I'd like to optimize. I have a regularization term, denoted by lambda If I set labda to 1000 I get a cost of 21 I I set lambda to 0.01 I get a cost of 10 However, If I ...
0
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1answer
83 views

How to handle Regression data thats not linear

I'm new to stats and am using Python 2.7 to fit a regression model (Random Forest). When I plot the percentile plot of the prices before and after a log ...
0
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2answers
46 views

Time Series Similarity : Differing Lengths with R

I am experimenting with creating a distance matrix between time series for clustering and similarity searching. The main reference I am using is for the Similarity procedure in SAS (Paper). I would ...
2
votes
1answer
77 views

Recommend classification algorithms to try

I am working on a binary classification problem that is reasonably-sized (100k observations). I extracted 60 numerical features; the classes in the training set are well balanced. There are some ...
1
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1answer
29 views

Features selection using F-score for multiclass classification

I'm going to implement a feature selection algorithm, and I plan to use the F-score for because of its simplicity. The problem is that, the F-score is used for binary classification. How can it be ...
0
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0answers
47 views

When to Log/Exp your Variables when performing Linear Regression?

I'm doing regression using Random Forests for predicting prices based on several attributes. Code is written in Python using Scikit-learn. How do you decide whether you should transform your ...
0
votes
1answer
48 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
2answers
60 views

How to measure weight similarity?

I'm doing some machine learning and get a set of optimum weights in the end. I'd like to verify that these weights are by and large the same no matter how many times I train on the data. I assume that ...
0
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0answers
54 views

Today's popularity of main data mining and machine learning tasks

In my dissertation about clustering, I would like to start with showing how clustering is becoming more and more popular in recent years in comparison with other data mining and machine learning tasks ...
0
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0answers
29 views

Is it possible for a learning algorithm to learn weights on a different scale?

I'm doing some simple regression. In my training I initialize my weights randomly. Then it converges to a minimum, but I noticed that depending on the initial weights, the algorithm seems to find the ...
1
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0answers
10 views

Iterated Conditional Mode approximation in E step of EM

I wanted to know what is the mathematical justification for using ICM as an approximation for the E step in an EM algorithm. As I understand in the E step the idea is to find a distribution that is ...
2
votes
1answer
70 views

How to compare two datasets using metrics drawn from unknown distributions and with small sample sizes?

I have two datasets consisting of metrics from several experiments. Dataset 1 is the collection of results of experiments E performed by user A on product A, repeated N times. Dataset 2 is the ...
0
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1answer
60 views

Converting J48 to if-then rules in Weka

I have a J48 decision tree model trained with WEKA. I would like to access the rules of the tree in J48 so that I can somehow use them in my code whether with if-else statements or as a decision table ...
1
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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
25 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?
0
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0answers
31 views

Difference of two gaussians [closed]

I got trouble understanding the following equation from a paper I'm currently studying [1]: $\pi_{ij} \equiv \int^{\infty}_0 \mathcal{N}(s|\bar{s}_i - \bar{s}_j,2\sigma_s^2) ds$ ...
3
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0answers
36 views

Reducing the dimension of an embedding

Let $O \in \mathbb R^{p\times m}$ be a data matrix of observations. Suppose we are given a model $\mu : \mathbb R^n \rightarrow \mathbb R^m$ which is able to approximately fit the observations. Fix ...
3
votes
1answer
57 views

Intutive difference between hidden Markov models and conditional random fields

I understand that HMM are generative models, and CRF are discriminative models. I also understand how CRFs' are designed and used. What I do not understand is how they are different from HMMs'? I read ...
1
vote
1answer
79 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 ...
-3
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0answers
38 views

How to calculate f-score, precision, recall and entropy for clustered data? [closed]

Any help will be greatly appreciated!
0
votes
1answer
58 views

Reasonable choices of programming languages and the length of program [closed]

I wonder how it's possible that: it can be shown that all reasonable choices of programming languages lead to quantification of the amount of absolute information in individual objects that is ...
5
votes
2answers
81 views

Why do categorical predictor variables in regression need to be recoded as multiple predictors?

I'm learning about machine learning using Python's library scikit learn, and in their tutorial here they mentioned about a categorical variable color which can have ...
0
votes
1answer
67 views

Why is Hedonic Regression used instead of Linear Regression

Why is Hedonic Regression used (especially in housing prices) instead of Linear Regression? There do not seem to be any libraries in Python (and R) for Hedonic regression, is it too niched a ...

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