Methods and principles of building "computer systems that automatically improve with experience."
0
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0answers
14 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, ...
-2
votes
0answers
53 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
votes
3answers
89 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
votes
1answer
53 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
votes
2answers
64 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
54 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 ...
1
vote
1answer
69 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
vote
1answer
43 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
votes
0answers
17 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
61 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
41 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
votes
0answers
26 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
votes
0answers
19 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 ...
1
vote
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, ...
1
vote
0answers
32 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
35 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
votes
0answers
68 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
votes
1answer
40 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
votes
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
40 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
votes
0answers
18 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
votes
1answer
82 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
votes
3answers
45 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
75 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
vote
1answer
24 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
votes
0answers
44 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
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
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
votes
0answers
53 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
votes
0answers
28 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
vote
0answers
9 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
68 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
votes
1answer
46 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
vote
0answers
36 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
21 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
votes
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
votes
0answers
34 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
49 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
74 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
votes
0answers
32 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
77 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
63 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 ...
1
vote
1answer
52 views
Forecasting optimization techniques in fantasy baseball
I am currently trying to build a better forecasting model for my fantasy baseball roster. I currently am using commonly accepted projected season statistics (ZiPS from Fangraphs) to determine the ...
1
vote
1answer
43 views
What to do against sampling/selection bias?
Let's say in machine learning the test cases are biased towards one or more options.
For example when there are 100 (just an example) test cases in total and every test case has a color to be ...
1
vote
4answers
122 views
Measuring representativeness of a sample using covariates
I was provided with quite a small sample of labeled (variable of interest) observations to train a model to predict unlabeled observations. All the observations are associated with many covariates. ...
2
votes
2answers
66 views
What's the formula of normalized correlation?
I read a paper and it use normalized correlation to evaluate the distance of two vectors. But I searched on the Internet and find seldom materials about normalized correlation, but I still got some ...
3
votes
0answers
36 views
Link Anomaly Detection in Temporal Network
I came across this paper that uses link anomaly detection to predict trending topics, and I found it incredibly intriguing: The paper is "Discovering Emerging Topics in Social Streams via Link Anomaly ...
1
vote
2answers
71 views
Split dataset randomly
I have a database with 500 records. I want to split these records to 75% and 25% *randomly*in order to use the different datasets for training and testing to machine learning algorithms. Does anyone ...
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 ...
