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

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When to use Bayesian Networks over other ML approaches?

I expect there may be no definitive answer to this question. But I have used a number of machine Learning algorithms in the past and am trying to learn about Bayesian Networks. I would like to ...
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1answer
10 views

Can we boosting or stacking with different input variables for each model in machine learning?

I have a question about Boosting and stacking in machine learning. Suppose that I will train neural network, SVM and logistic regression using optimization algorithm to optimize best inputs in first ...
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6 views

Gaussian Mixture Model with Custom Distance Metric

I have some 1D data that I want to cluster using Mixture of Gaussian. However, the data "wraps around" at two extremes. Specifically, I have a list of angles from $-\pi$ to $\pi$ and the data near two ...
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1answer
53 views

Why is there a E in the name EM algorithm?

I understand where the E step happens in the algorithm (as explicated in the math section below). In my mind, the key ingenuity of the algorithm is the use of the Jensen's inequality to create a lower ...
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10 views

finding weights of aspects

I have aspects(nouns) from online customer reviews of a product. I have done sentimental analysis to get polarity of each aspect. Now I want to weight the ...
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39 views

When should I use feature selection and when should I use dimensionality reduction techniques?

When should I use feature selection and dimensionality reduction? I know that feature selection is different from dimensionality reduction. But I don't know under what circumstances should I use ...
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8 views

How to apply AIC to a situation where the mean of a multivariate normal is a 0-1 d-dimensional vector with exactly k 1's

I am trying to apply AIC to estimate mean in the following case: Let us consider that I have $n$ random variables $X_1, \ldots, X_n$, drawn i.i.d. from a normal distribution of mean $\mu\in\{0,1\}^d$ ...
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4 views

Weka - StringtoVector Filter Not working [migrated]

I am practicing Weka using the Reuters data. The StringtoVector Classifier works for converting my string data (shown below), so I can analyze the articles to understand what words predict the ...
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10 views

Comparing the impact of 2 independent variables on the dependent

I'm using a predictive modelling technique which has 2 parameters. I've performed a sweep of values for each of these 2 parameters, running each permutation of parameters 30 times as the technique is ...
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1answer
30 views

Regressing a discrete variable

I have a discrete dependent variable (say, number of units bought) and want to run a linear regression with in-store promotion, seasonality, trend etc. as predictor variables. I'm not sure if it is ...
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1answer
38 views

basic question about machine learning and probabilistic framework?

Why do we assume that the pair (X,Y) where X and Y are features and labels respectively are random variables governed by a probability distribution? Why does this assumption make sense? What if the ...
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40 views

Why would a regression model predict super huge numbers?

I have a set of 55 items. Each item is defined by 6 values. I am doing 55-fold cross validation: training a model on 54 items, predicting on the 55th. The 6 values of the 54 items are used in some ...
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17 views

What are the advantages of using logistic regression with kernel over others?

What are the advantages of using logistic regression with kernel over others type of logistic regression(e.g.,dot)?
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1answer
53 views

How to transform categorical variable into numerical variable when using SVM or Neural Network

To use SVM or Neural Network it needs to transform categorical variables into numeric variables, the normal method in this case is to use 0-1 binary values with the k-th categorical value transformed ...
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8 views

neural networks in R with multiple entries for one id [migrated]

I have a dataset with multiple rows for one id. The multiple row signifies the reading in each month. and there are multiple id's in the same fashion. eg id,t1,x1,x2,x3,y A,1,2,3,4,5 A,2,4,2,6,5 ...
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11 views

Item based collaborative filtering when items have been available for different lengths of time

I am attempting to use item based collaborative filtering for product recommendation. The matrix is all 1s and 0s based on whether or not a buyer purchased an item, and I am using cosine similarity to ...
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5 views

How to handle multiple experiments or same row/entity?

I have data that was gained from experiments preformed by n individual persons. So for each item in the experiment I have n values for the same variable. Note that IMHO averaging the values does not ...
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24 views

what's the best empirical macro/micro F1 score?

Theoretically it should be 1. In the following presentation it's said that "0.5 to 0.55 (micro) F1 score is tops for multi-label classification problems" I tried to investigate this statement but ...
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15 views

Are there differences between Delta TF-IDF and TF-IDF?

Are there differences between both algorithm or not ? i mean if i implement for ex Delta TF-IDF in a project instead of ...
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31 views

How do I classify data with multiple dimensions using a gaussian classifier? [on hold]

I've computed the equation inside the brackets (but not i): Features=dimensions (x,y)..R^n Ck being the covariance matrix, z being the input vector, u being the mean vector, N being the number of ...
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1answer
70 views

Seeking a free symbolic regression software [on hold]

Now that Formulize / Eureqa started charging $2500 a year for using it and having crippled the trial version, does anyone know of any replacements that can do similar things like find an equation ...
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30 views

Linear Regression using statistical method or gradient descent? [on hold]

Parameters for a regression line can be calculated using gradient descent , normal matrices operation and also it can also be calculated using statistical means where slope and intercept are ...
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11 views

Perceptron Algorithm, RMSE just cycles through two numbers

My input is a bag of words feature vector, of the form: Example: Document 1 = ["I", "am", "awesome"] Document 2 = ["I", "am", "great", "great"] Dictionary is: ...
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25 views

Testing hypothesis when each data instance is a vector

The experiment involved studying population body velocity changes during a marathon . Velocity of each person in control group and experiment group were measured at multiple instances during marathon. ...
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1answer
20 views

How to handle changing input vector length with neural networks

I want to train a neural network with a sequence of character as an input vector. Learning examples have different length and for this reason I don't know how to represent them. Let's say I have two ...
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1answer
30 views

What is the difference between independent variable and a feature?

I ran into this question which asks the identification of various terms for a linear regression function (f). I am confused about the "independent variable" definition. What is the difference ...
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1answer
71 views

How to pass the sequential var. length data for the NN?

The main task is from Inductive Logic Programming (ILP) area. The task related to ANN is inspired by paper below but is applied to more complex case.Learning an approximation to clause evaluation ...
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1answer
30 views

Automatic feature building/extraction

I have a large time stamped data set (several millions of rows), with known measured inputs xi, where i is a large number to the order of magnitude of 20. The goal is to predict a response yi given ...
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1answer
45 views

Why is lambda plus 1 standard error a recommended value for lambda in an elastic net regression?

I understand what function lambda performs in an elastic net regression. And I can understand why one would select lambda.min, the value of lambda that minimizes cross validated error. My question ...
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2answers
67 views

Can I safely use variable importance of a random forest in a paper?

Background: I just started with machine learning and I'm considering using it on old data based on which I'm writing a paper. The paper deals with radiation-induced lung damage and the data comprise ...
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25 views

Deriving step size/learning rate in the hinge loss passive-aggressive/perceptron algorithm

Recall the perceptron algorithm: cycle through all points until convergence $\text{if }\, y^{(t)} \neq \theta^{T}x^{(t)} + \theta_0\,\{\\ \quad \theta^{(k+1)} = \theta^{k} + y^{(t)}x^{(t)}\\ \}$ ...
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50 views

Maximum likelihood estimate parameters estimation

In this tutorial on mixture models, page 2, how did the author arrive to the parameters for maximum likelihood in the fully observed case? This is the general setting (based on an excerpt from the ...
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2answers
30 views

Using ROC curve for balanced data

I understand that using the area under the ROC curve is a useful error measurement for unbalanced data. What happens if we use it for balanced data?
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1answer
33 views

Machine Learning algorithm to predict user's location

I'm planning to develop a location-based marketplace app where user will see all products sold in their locations. The easiest thing to do is by using the HTML5 Geolocation. But some times user don't ...
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1answer
15 views

Posterior probability of an image when the posterior of local features are known

Lets assume the local features $x_1, x_2, \dots x_n$ of an image $I$ are independent. I know if $p(x_i|c)$ are given $p(I|c)$ can be defined by $\prod_{i=1}^N x_i$ But I dont know how to calculate ...
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21 views

What is the point of Subtree raising? [closed]

Why would it be useful to turn on sub-tree raising inside of Weka for a J48 decision tree? How would this affect the data as opposed to having sub-tree raising turned off?
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16 views

Should I normalise (to sum to 100%) the MSE reduction for variable importance in a random forest?

I am investigating the importance of a set of variables in a linear model. I am conducting a random forest analysis and using the permutation-based Mean Square Error (MSE) reduction as a measure of ...
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12 views

Is this a correct way to do document classification using topic modeling?

I am using LDA to extract topics. I want to do topic modelling and use the topics as features to do document classification. I am proposing the below approaches using scikit-learn. I want to know ...
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30 views

Finding score of aspects

From online customer reviews, I have extracted aspects(nouns). I have sentiment lexicons(list of positive,negative words). Example ...
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0answers
9 views

What text summarization algorithm or practical application do you use? [closed]

I'm looking to implement a text summarization system for multi-page documents. Is there a method, API or algorithm set that you know of that works the best?
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6 views

What are interesting open questions in sub modularity in the context of Machine learning?

I was interested in knowing about open question in sub modularity, specially in the context of inference and machine learning. I was wondering if anyone knew about any interesting open questions ...
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14 views

How do I do RLM normalization in R?

I have a combined dataset of 123 samples and 9,482 features (expression levels of antibodies from Invitrogen's ProtoArray v5.0). Based on ...
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1answer
14 views

train multiples observations from the same person in caret

I have data where persons were give four different tasks under three different conditions (intensities). The data looks like this: ...
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7 views

What is the fastest way to get the document term matrix in scikit learn?

I am using scikit learn CountVectorizer on top of ~11K documents, each of size ~5000 words. It takes ~ 1 hour to generate the tdm (document term matrix). Is there a much faster way to generate the ...
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1answer
23 views

Imputation of missing data before or after centering and scaling?

I want to impute missing values of a dataset for machine learning (knn imputation). Is it better to scale and center the data before the imputation or afterwards? Since the scaling and centering ...
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8 views

Need to order a set of colors to build a feature vector

I am working on Computer Vision task of object classification with python and OpenCV. Currently I am extracting some characteristic colors of an image using K-means clustering on all the pixel to ...
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1answer
255 views

Why doesn't a non-linear kernel improve accuracy in high dimensions compared to a linear kernel?

I read somewhere that if the number of dimensions in your feature set is very high, then a non-linear kernel such as RBF (or any other) may not help in increasing accuracy compared to a linear kernel. ...
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14 views

Likelihood convexification

I am doing constrained vector optimization using a non-convex non-linear likelihood function. My problem is of the following form: $$\begin{align*}\hat Q &= \underset{\vec Q}{\arg\min} -\log ...
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implementating the bayesian linear prediction with NIG prior

In Bayesian linear regression when the covariance of weights is unknown; one can set Normal-Inverse-Gamma prior. Based on "Machine Learning: a Probabilistic Perspective", Page 235, \begin{equation} ...
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1answer
14 views

How to incorporate time dimension in a recommender engine?

I have very long history of user behaviour, when they choose to buy one of the 50 products. I want to take in account that if a user bought product1 two years ago and product2 yesterday, second ...