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

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Observed versus hidden variables in this particular context

I am a novice in Bayesian networks. I have a problem which is best described (at least I think so) in the following story. One wants to predict earthquakes. Let's say it has 5 variables, the last one ...
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28 views

When to use K mean clustering and hierarchical clustering algorithm? [on hold]

Can you please tell me when to use the K-mean clustering and hierarchical clustering algorithm and what is the different between them... Regards, Rahul
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19 views

Explanation on a Minsky's critique on statistical learning related to XOR

I was listening to the first session of society of Minds by Minsky (2011) and he mentions at some point around minute 48 the following: "...lots of statistical learning tools is good for lots of ...
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2answers
16 views

Inferring on an unknown number of function approximation

I want to ask whether a procedure to do the following job exists (or whether it makes sense for it to exist). First, assume we have $k$ functions $f_1,...f_k$ that have the same domain and range. ...
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23 views

Find distribution of Bus arrival time

I am currently working on a problem in my research which can be modeled into the following question: Let's say I have a rich dataset with values for the variable $A$ which is equal to ...
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6 views

Machine learning/random forests with noisy response data

Machine learning techniques like random forests seem to assume that the responses in the training set are known perfectly. Specifically for regression applications, it seems one needs to account for ...
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6 views

Viability of software dev - Use of and requirements of NN

Hello I would like to know this two things regarding the viability of producting a software, so: 1) Are available on internet some OCR libraries for free? Can I train my own NN having only a laptop? ...
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1answer
21 views

What is a good model for revenue managment / price optimization problem

I am trying to create a dynamic pricing model to optimize revenue for a hypothetical business. Lets say I have an application that connects dog walkers to people who want to pay for their dog to get ...
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1answer
25 views

make prediction with HMM

I want to use HMM to make some prediction. say $O$ is the observation, $S$ is the hidden states, and I know how to train the model with forward-backward algorithm. I just get confused with how to ...
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1answer
46 views

K-means clustering feature selection

I have a set of English and foreign language documents that I would to perform k-means clustering on to find document groups by topic. These documents are concatenated social media comments for ...
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1answer
39 views

One model performs better than the other. How to measure if it is statistically significant?

So, let's say that I train two models on the same dataset. I run the experiment once and I get the following results: Using a Neural Network I get an AUC ROC of 0.941. Using Random Forest I get an ...
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2answers
34 views

What is the difference between a neural network and a perceptron?

Is there any difference between the terms "neural network" and "perceptron"?
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1answer
59 views

Are there any contemporary uses of jackknifing?

The question: Bootstrapping is superior to jackknifing; however, I am wondering if there are instances where jackknifing is the only or at least a viable option for characterizing uncertainty from ...
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8 views

svmlight for unbalanced data [on hold]

I'm using svmlight for multiclass classification using one vs rest strategy. I'm having highly unbalanced data. One data set has 5000 and other set has 500.How to train this unbalanced data in ...
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1answer
53 views

What does the k-value stand for in a KNN model?

What is the k-value in a KNN classification model? Is K the number of Clusters?
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27 views

What are the potential disadvantages of doing kernel PCA?

I was trying to learn more of the motivation around kernel PCA. Its clear to me that one might need to change the representation of the data if it lies in a non-linear space, hence, the projection ...
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0answers
8 views

Bayesian network overfit - number of features and examples

For a dataset consisting of 150 examples (mostly binary features) what would be the number of features needed so that a Bayesian network doesn't overfit? I know there is no exact answer and I've ...
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1answer
24 views

Is there AUC for neural network?

I am confused about how to calculate AUC for neural network with a softmax classifier. For example, I know that for SVM, we can change the threshold value and determine the AUC. WHat about in neural ...
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6 views

Number of parameters in multinomial logistic regression

In Chapter 10 (Directed graphical models) of Murphy's Machine Learning text, the author claims that multinomial logistic regression has $O(K^2 V^2)$ parameters, where $K$ is the number of discrete ...
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22 views

What are the most interesting, new hot topics in machine learning for seminar and later for thesis? [closed]

I'm in an informatics master student in Palestine Polytechnic University, I have a seminar course this semester i cant decide the topic to search in, especially that i want it to be my thesis topic ...
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15 views

Convolutional Neural Network Performance - Cats & Dogs

I am currently experimenting with a Convolutional Neural Network, trying to get a good performance on the Cats & Dogs challenge at Kaggle. By now, the best result I could get using my network was ...
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3 views

removing batch effect when combing patient's data into a large cohort

I have some clinical data quantifying severity of disease for patients from 3 different hospitals. Basically, the patient severity vector for each hospital looks like below: ...
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18 views

Suitable Model for predicting flight delays in R [closed]

I want to predict the flight delays.Which classifier or which machine learning algorithm i have to use for predicting the flight delays in R and please guide me how to find the accuracy of that ...
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1answer
172 views

Why do we divide by the standard deviation and not some other standardizing factor before doing PCA?

I was reading the following justification (from cs229 course notes) on why we divide the raw data by its standard deviate: even though I understand what the explanation is saying, it is not clear ...
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1answer
21 views

Bias/variance tradeoff tutorial

I'm looking for a good tutorial about bias/variance tradeoff. In particular, I'd like to find someone that explains how different algorithms in machine learning play in this tradeoff, and possibly how ...
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1answer
50 views

Summer school on data mining & ML [closed]

I'm a PhD student in Physics and this summer I'd like to attend a one/two weeks summer school on data mining and machine learning. Do you have one to recommend? Thanks!
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2answers
26 views

Machine learning algorithms for continuous response variables? [closed]

It seems like common ML algorithms like SVM, Naive Bayes, Neural Nets, Logistic Regression, etc. are designed to predict categorical responses, ie. (1,0). However, I haven't run into an algorithm that ...
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20 views

Tutorials on main machine learning algorithms [closed]

I was asked during an interview to give my favourite machine learning algorithm and describe it. For different famous algorithms (like decision trees, svms etc), which paper would you suggest to ...
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1answer
35 views

combining multiple classifiers common features

Can multiple binary-classifiers be combined to produce a final output if their feature sets have some common elements? How will this influence the accuracy?
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2answers
26 views

Emphasize a link between two predictor variables (Machine Learning)

I am creating a machine learning application which will utilize logistic regression (though I haven't ruled out bayesian regression). I have multiple predictor variables that I believe to be ...
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2answers
77 views

Kernel density estimation vs. machine learning for forecasting in large samples

This is a hypothetical and pretty general question. Apologies if it is too vague. Suggestions on how to better focus it are welcome. Suppose you are interested in the relationship between one ...
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35 views

Machine learning approaches for data from different time periods

As a general question what, would be machine learning techniques to cope with observations of a population from different time periods? For example, I have 10000 observations of 500 features from ...
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3 views

When to use index and seeds arguments of train() in caret package in R [migrated]

Primary Question: After reading the documentation and google searching, I am still stumped as to what the situations are where it is advisable to pre-define resampling indices such as: ...
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0answers
9 views

Academic source for precision/recall/F-Score [duplicate]

I know that precision/recall/F-Score are widely used to evaluate Machine Learning algorithms' performance. I also want to use it for my Master's Thesis and I am searching for an appropriate source to ...
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22 views

SVM fusion training data set

For a binary classification problem, I have split the data set into multiple sets and trained each set using a SVM. I want to combine the outputs from each data set using another SVM. What is the best ...
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26 views

Statistical comparison of multiple prediction models

I have a rather limited data set where for 100 subjects 30 attributes were measured before surgery and one attribute ($y$) was measured after surgery. About 20% of values are missing. The goal is to ...
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1answer
16 views

Name and methods for classification with 'unknown' as acceptable result

What is it called when, in a classification task, it is acceptable that some data-points do not receive a label? And what classifiers are suitable? I have a dataset with a two valued target variable. ...
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2answers
48 views

Should image classifier be trained using colormap pixels or the actual value?

For example, I have a population density map of a 100 x 100 km square region. Each part of the rectangular region represents the population density i.e. (1,1) -> 128 people, (100,100) -> 50 people ...
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13 views

Non idependence within groups

I have to train a machine learning model for classifying two groups. Unfortunately, my positive group has a small number and many cases are not independent from each other (observations taken in ...
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10 views

Unbalanced groups and classification errors

I would like to adopt a general strategy for dealing with an very unbalanced dataset, where my "positive" group corresponds to 1/40 of all the observations. The reason why I ask it is because all the ...
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1answer
35 views

Using monthly product usage data to predict customer churn

I've been reading tons of papers detailing methods on predicting customer attrition, but none of them have mentioned using product usage data over time. We keep detailed logs of how many times User A ...
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1answer
45 views

Mortgage loan predictive analysis

I have hundreds of thousands of mortgage loan historic records that look like these 2 examples: ...
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1answer
30 views

How to normalize filters in convolutional neural networks?

Usually when convolving images the elements in the filter sum to one. Does this creteria enforce in convolutional neural networks? If yes, How?
2
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24 views

ML classification problem for matrix and distribution estimate for each cell in the matrix

I am trying to think about a machine learning/statistical learning related problem. But would love to get idea from people in the forum about related problem/work/resource. So, the problem idea is ...
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18 views

What are the state-of-the-art algorithms for training Conditional Random Fields?

What are some of the algorithms for learning the parameters of a Higher Order Conditional Random Field (such that the label Y_i depends on the labels Y_(i-1), Y_(i-2),...,Y_(i-o))? I am looking for a ...
2
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1answer
26 views

How to convert the objective function to canonic form of sparse coding?

As we know the conventional sparse coding problem (LASSO) is: $\min_{\alpha} \| X-D\alpha\|_F^2 + \lambda \|\alpha\|_{1} \tag{1}$ where $X$ , $D$, and $\alpha$ are data, dictionary and coefficients ...
2
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1answer
28 views

Test Error less than cross-validation error-implications?

If the test-set RMSE error of a model is less than cross-validated RMSE error, how can I interpret this? Is this abnormal? Does it imply a mistake in the methodology?
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1answer
115 views

Combining several variables into one outcome score: How is it done in the machine learning community?

I have got 8 cognitive (continuous) behaviour variables and would like to combine them into a composite score. I would then like to find the best predictors of this outcome (from about 50 predictors). ...
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44 views

Why is the most probable assignment for all variables in MRFs called MAP assignment?

I am new to graphical model, especially Markov Random Fields. I have a question about MAP assignment. Let say we have the graph structure and all the potential functions. MAP estimation is finding ...
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21 views

What algorithm should be used for intelligent route planning system based on drivers experience

I am new here and I'm a student, my project is on machine learning for an intelligent route planning algorithm. Basically the summary of my project is to use a machine learning algorithm and ...