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

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Clustering Self Organising map nodes

How can i perform unsupervised clustering on the nodes after i have run self organising map. Assuming i have no ground truth of the dataset. I read Density-based Simultaneous Two-level Algorithm for ...
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10 views

Trying to perform cluster analysis based on multi-variable data?

I’m struggling with how to find clusters/groups in a large set of multivariable data. Problem: Let’s say I have an ecommerce candy store. At my candy store I have various brands of candies(kitkat, ...
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8 views

How to predict latitude and longitude

There are some models that can take multiple predictands (I believe there is a variant of RandomForests in Python that does this). Are there other ways? If we reduce dimensionality we will lose ...
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10 views

Marginal likelihood and coordinate ascent

When updating posterior distributions in Bayesian inference using coordinate ascent, is the marginal likelihood of the data guaranteed to increase after each update?
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32 views

Help logistic regression [on hold]

I have to predict a binary variable with logistic regression. The idea is to classify a number of subjects each either sick or not sick. Therefore, I have 11 risk factories for each person and have to ...
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11 views

On choosing the size of test set for an (already collected) dataset

When I search online for criteria to select the fraction of a dataset that should be set aside as test set, invariably the answer boils down to "it depends" (on the type of data, the type of study, ...
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6 views

Training an Elman Recurrent Neural Network?

I have few doubts related to training Elman RNN using Backpropagation Through Time Algorithm. Assume, I present a sequence to the network and the network adapts the parameters based on the error ...
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9 views

online bin packing and machine learning

I am trying to optimize scheduling disk allocation in data centers. In nature it is an online bin packing problem. But, since each customer has it's own behavior or kind of workload I was wondering, ...
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1answer
57 views

How to detect noisy datasets (bias and variance trade-off)

Studying the bias-variance trade-off: expected loss = bias + variance + noise I understand that we minimize this quantity by finding the "best" balance between ...
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1answer
40 views

Understanding neural networks and classes

I want to know if this argumentation is valid or not of my algorithm. I'm trying to implement a CBIR (Content-Based Image Retrieval) where I've used the basics on CBIRs (colour, texture, shape, ...
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1answer
44 views

What makes the recommendation problem unable to be solved by traditional machine learning algorithms directly?

We have collaborative filtering and content based algorithms there for recommendation. What stops traditional algorithms from directly being used to find missing values in the Utility matrix ...
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1answer
12 views

How to train a Recurrent Neural Network for Temporal XOR?

I have coded a Elman RNN using BackPropagation Through Time. In order to check my implementation, I have chosen Temporal XOR(a sequence of binary digits with the third being the xor of previous two ...
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18 views

Machine Learning when missing data is state-dependent (e.g. adaptive questionnaires)

I have the following problem: I am dealing with an adaptive questionnaire, meaning a questionnaire where there are questions that are only asked when a previous question had a specific answer. The ...
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1answer
35 views

When using SVMs, why do I need to scale the features?

According to the documentation of the StandardScaler object in scikit-learn: For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support ...
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19 views

Maximum Likelihood Algorithm Implementation [on hold]

Hello Guys i have to implement Algorithm of Maximum Likelihood using Hyper-spectral data of 170 bands, have to classify 16 classes using ground truth data. The formula for implementation is as follow, ...
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17 views

Why does Alex Graves use a mixture model with his RNN instead of just directly predictive real values?

Alex Graves created a model to generate hand writing sequences which use an LSTM (kind of Recurrent Neural Network) to predict the parameters for an mixture model. The mixture model is then used to ...
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7 views

Intuition behind using boolean frequencies in tf and multiplicities in tfidf in gensim

I have a general question about the intuition behind the implementation of the tfidf model in gensim. I understand that by default gensim uses as term frequencies Boolean frequencies, i.e. 1 if term ...
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1answer
28 views

classification error in supervised learning problem

Suppose you have a supervised learning project where it is not easy to check whether the value you predicted is correct or not. So, in this case, does it still make sense to talk about the ...
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2answers
62 views

Machine Learning Methods for Binary Classification

I was hoping to get a nice list of alternatives to logistic regression and decision trees for binary classification ("Yes vs. No" or "Cured vs. Not cured"). I am more interested in identifying the ...
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15 views

A book on digital signal processing and control theory for machine learners?

I noticed that digital signal processing (DSP) and control theory (CT) are essential to master machine learning, at least I saw that many great machine learning scholars are experts in these two ...
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20 views

[CNN]: Predict label of one single image using DeepLearnToolbox [on hold]

I am using DeepLearnToolbox to do CNN (Convolutional Neural Networks). I have computed my network successfully and I've seen my accuracy, but my question is: How can I query one single image into ...
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13 views

What are are some common concepts regarding optimizing a algorithm with forecasted input?

I am a programmer working on a problem where I need to make an optimization based on forecasted data. I feel kinda lost, and would like to find some statistics/machine learning articles on similiar ...
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9 views

**Kappa measure in Random Forests** [on hold]

Following is the detailed summary of trained model by Random Forests: ...
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1answer
29 views

How to tune parameters through cross-validation without grid search?

There are actually lots of questions about parameter tuning through cross-validation. I have read some of them, e.g. this one. I, however, still can't understand the details of the process. Here are ...
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1answer
37 views

Variational Inference: good inference but ELBO decreases instead of increasing

I am playing with Variational Inference for clustering within a mixture of Gaussians. My first implementation seems to work fine (this is for the geyser dataset): ...
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9 views

How to calculate the probability of a sample to get a specific pattern, given all samples and features?

I have a data aggregation tool, which collects information on many samples (clusters of molecules in this case). So it can create a sparse matrix of NxM binary values (N answers of 0 or 1 for M ...
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23 views

Error function of two layer neural network

Let's say we have a two layer neural network with $\theta^1$ and $\theta^2$ as the layer units. Now ${\delta J \over \delta \theta^2}$ is convex and has a global minima for each unit. But what is the ...
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45 views

Is validation set always necessary?

Lets say I did the following steps: Used some separate development set to select some features. Decided a priori to use only one learning algorithm (SVM) with only default parameter values. Trained ...
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16 views

Different Methods for clustering skills in text

Consider a talent pool in which each member has some set of skills. Some of these talent are submitted to orders as potential candidates of which one is selected. It is reasonable to assume that the ...
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2answers
55 views

How to use cross-validation with regularization?

I think I understand each of these concepts (cross-validation, regularization) independently, but I'm not quite clear on how they can be put together in practice. Loosely speaking, in ...
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1answer
45 views

How to simulate more data for machine learning?

I am attempting to analyze a small dataset using machine learning (SVM, binary problem). There are $103$ samples and $215$ variables (all variables are real numbers). Some of the variables (around ...
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22 views

Data augmentation techniques for general datasets?

In many machine learning applications, the so called data augmentation methods have allowed building better models. For example, assume a training set of $100$ images of cats and dogs. By rotating, ...
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27 views

How to model the relation between two variables based on other common relations?

I am trying to model an inference problem, but it doesn't seem to readily fit with the algorithms we usually hear about. I am hoping that I have missed something, and someone hear can point out that ...
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24 views

Does this simple anomaly detection algorithm work for > 100 features?

In Andrew Ng's Coursera class on Machine Learning, we learned to use a Gaussian distribution $p(x) = \prod_{j=1}^n p(x_j,\mu_j,\sigma_j^2)$ to detect anomalous examples when $p(x) < \epsilon$ ...
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1answer
36 views

Opportunities in machine learning and computational intelligence [closed]

I'm not sure this is the right site to post my question. If not, please direct me to the right one. I'm interested in machine learning and computational intelligence. I've spent the last year of my ...
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32 views

Feature extraction based on correlations

I have a small problem regarding feature extraction with correlation. I have divided my question in four parts hoping that somebody can help me. I have a dataset consisting of fMRI images. Each image ...
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2answers
34 views

No regularisation term for bias unit in neural network

According to this tutorial on deep learning, weight decay (regularization) is not usually applied to the bias terms b why? What is significance (intuition) behind it?
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2answers
48 views

Recurrent vs Recursive Neural Networks: Which applies better for NLP?

So, we have Recurrent Neural Networks and Recursive Neural Networks. Both are usually denoted by the same acronym: RNN. According to Wikipedia, Recurrent NN are in fact Recursive NN, but I don't ...
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1answer
33 views

Neural Networks and Numeric Prediction

I'm new to machine learning and am trying to write a simple neural network that uses back-propagation. Now, so far I've successfully implemented my neural network to learn a boolean function. So for ...
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33 views

Estimating probability distribution function of data stream

Although a similar question exists, I couldn't find my answer. I'm not a statistician hence please neglect if some terminologies aren't correct and let me know if I am interpreting something wrong. ...
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34 views

How to find a cost function with only a statistical measure of success?

Using the U.S.A. as a loose analogy, we have search algorithms that find the names and number of States adjacent to a given State (containing a selected city). The goal is to minimize the number of ...
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2answers
75 views

Sequential pattern mining on single sequence

Can someone give me a hint about a good approach to find a frequent patterns in a single sequence. For example there is the single sequence ...
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1answer
21 views

Intuition behind sparsity in over-complete sparse auto-encoders

I am trying to get a grasp of the intuition behind the sparse representation used in over-complete auto-encoders. One piece of text that offers a somewhat intuitive explanation is from ...
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9 views

Interpreting the lift curve

Suppose we have two classes: A and B. Suppose we use a logistic regression to assign each unit to A or B. The curve lift is calculated through this formula: $\frac{n_{22}/n_{.2}}{n_{2.}/n_{..}}$ ...
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1answer
12 views

Is there a version of Latent Class Analysis with unspecified # of clusters

I understand that you can use the elbow method to plot LCA solutions vs log likelihood to figure out, at which k, it is no longer worth it to add more clusters. And I will resort to this if need be. ...
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88 views
+50

Recurrent Neural Network (LSTM) Questions

I am coding an RNN-LSTM module, and I have the following questions: 1) In non-linear input like one-hot-encoded words, you use an extra sign (like <eol>) at ...
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19 views

Measuring effectiveness of marketing through attribution analysis [on hold]

My data(dataframe in R) looks like this:The data is ordered by CustomerName and then TimeofEvent. ...
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1answer
39 views

Predicting the job switching period of an employee

I have a machine learning problem to solve. Given the data about employees, is there a way by which we could possibly predict that when an employee is going to switch his current job? We can make use ...
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1answer
39 views

Is building training data set from unlabeled data considered as a scientific contribution? [closed]

Is building a training data set, from unlabeled data, for a machine learning classifier considered as a scientific contribution?
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31 views

What are the differences between autoencoder and t-SNE?

As far as I know, both autoencoder and t-SNE are used for nonlinear dimension reduction. What are the differences between them and why should I use one versus another? thanks!