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

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How to shorten the detection time of adaboost algorithm?

I'm working on a license plate detection using OpenCV's adaboost algorithm. However, after training, it shows that the detection takes 3200ms for a single image, where the image size I used is ...
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Clusteriod questions

I would like to clear some things up because I'm confusing everything. A $clusteriod$ is a coordinate for the mean value of a cluster? So if I have a 2-d .csv file I wish to perform kmeans, the ...
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Identifying topic name

When we perform Latent Dirichlet allocation on a set of documents to cluster, just assume give n=5 topics. How can we assign the topic name for these topics after performing LDA? I would like to give ...
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Diference between glm and bigglm estimates

How does bigglm function in biglm package work for logistic regression? I thought that it is not possible to calculate LR on chunks of data and then merge results. Will glm and bigglm yield ...
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ANNs and mixed data-type problems

I did some research but I'm not quite sure if ANNs, more precisely MLPs, are able to handle mixed data-types (e.g ordinal and metric scaled variables) like in the German Credit data set without ...
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EM/K-means task at hand/confusing

Hello I am getting into machine learning and patter recognition, however it's still quite a jungle at the moment. I am using WEKA and Java to try and create my first program. The following is what ...
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XOR backpropagation convergence

I've implemented 3 supervised training algorithms: rprop, online- and batch backprop with momentum. I have the simple XOR test, and I measured how many times they converge out of N iterations. My ...
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64 views

Deep learning algorithm

What's the difference between deep belief network and deep convex network?
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24 views

Machine Learning approach to solve this selection problem?

I have a set of items S. items can be joined to groups consisting of up to x items. For a group of items i can derive a score Y using some unknown performance measure. What would be the most efficient ...
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7 views

How can recurrent neural networks be used for sequence classification?

RNN can be used for prediction, or sequence to sequence mapping. But how can RNN be used for classification? I mean, we give a whole sequence one label.
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How to Mine Tree Structures?

To learn similarities/differences between different instances (that are in the form of tree), what are the suitable methods/approaches? I know kernel methods and particularly tree kernels, but would ...
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1answer
44 views

Types of artificial intelligence with good results [on hold]

I have been looking into artificial intelligence for some time now. I am wondering what branches are still in active research and have some good/interesting results. The two that I have looked in so ...
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ImageNet: what does top-five error means?

One of the evaluation method for ImageNet Competition (classify 1,000 categories images) is top-5 error, what does that mean? See: http://www.image-net.org/challenges/LSVRC/
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26 views

Plot that shows which attribute has the most effect on class?

I'm playing around with two datasets: Mushrooms and Breast Cancer. I'm trying to form a hypothesis of which attribute would have the most effect on the class when making predictions about the class. ...
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5 views

Nonnegative Matrix Factorization coefficient matrix normalization in microarray data

I am confused by the coefficient matrix in nonnegative Matrix Factorization. Suppose I have a time course microarray data matrix as V (m*n), which is decoded by ...
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Confidence Interval of Calculating Precision

The problem I'm trying to solve has the following setting: Let $X_1, \dots, X_N$ be a data set where each $X_i \sim Categorical(p_{TP}, p_{FP}, p_{TN}, p_{FN})$ s.t. $\sum\limits_{k \in ...
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1answer
14 views

Distance from hyperplane in SVM rbf kernel in R

I am running ksvm in R(using kernlab package) for a highly imbalanced data.Is there any way i can get the distance of my test data points(each of dimension 8-10) from the hyperplane?so that i can ...
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21 views

How to fit a single quadratic term to a regression

I have a high dimensional multivariate model and am fitting linear weights to each of the $N$ free variables using a classic stable SVD matrix solver. This works. I want to improve the fit by using a ...
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10 views

What techniques are used to prevent overfitting in DSN

A Deep Stacked Network (DSN), is a ensemble learner, which roughly works by training a single hidden layer neural network on the inputs and target outputs, then training another which takes an input ...
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1answer
21 views

A single document as input to LDA?

We use topic modelling usually on a collection of documents - which makes the input. But what if I only have a single document where I want to see the underlying topics in it? I have heard that you ...
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Non parametric estimators for noisy functions

Suppose there is a function $f(a,b,c,\ldots)$ of $M$ variables (fixed numbers, not random variables). Add some Gaussian noise to this function: $$ g(a,b,c,\ldots) = f(a,b,c,\ldots) + ...
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28 views

How to use geometric proximity in classification

I am doing a classification of certain regions of an image. Let's say I have done the classification, and some classes have been classified positively (negatively) with high probability. For my ...
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1answer
40 views

Gradient of Log-Likelihood

Considering the following functions I'm having a tough time finding the appropriate gradient function for the log-likelihood as defined below: $a_k(x)=\sum_{i=1}^D w_{ki}\cdot x_i$ $P(y_k|x) = ...
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Classification problem with constraints

I am trying to solve a classification problem with constraints and need advice on how I should approach it. Here's the problem: Given N observations, FLAG_j, j=1,..,N (this is a binar variable), and ...
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715 views

What exactly is a Bayesian model?

Can I call a model wherein Bayes' Theorem is used a "Bayesian model"? I am afraid such a definition might be too broad. So what exactly is a Bayesian model?
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23 views

Reparameterization of probability distribution (spike and slab)

I try to understand a statement in this paper: http://papers.nips.cc/paper/4305-spike-and-slab-variational-inference-for-multi-task-and-multiple-kernel-learning.pdf In particular, I am talking about ...
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17 views

Feature engineering with non-fixed length vectors?

I have a bunch of data that looks like this: ...
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max margin vs max posterior/likelihood advatages

I am working on some parameter learning approaches for image classification. What is the differences between the following two for image classification? max margin methods maximum likelihood/ ...
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2answers
39 views

Mixing proportion $\pi$ in Mixtures of Gaussians

I am trying to understand a little better mixtures of Gaussians and their generative approach in general. For a mixture of Gaussians we start with this formula: $$p(x)=\sum_{k=1}^{K}\pi_{k}\cdot ...
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Profiling high-scoring clusters in a multi-dimensional feature space

I have a large amount of samples, which have a multi-imensional feature vector associated with them. Each sample has a "score", and the length of the feature vector is substantial (n>100, and in ...
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14 views

number of feature maps in convolutional neural networks

When learning convolutional neural network, I have questions regarding the following figure. 1) C1 in the layer 1 has 6 feature maps, does that mean there are six convolutional kernals? Each ...
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2answers
142 views

Times series analysis vs. machine learning?

Just a general question. If you have time series data, when is it better to use time series techniques (aka, ARCH, GARCH, etc) over machine/statistical learning techniques (KNN, regression)? If there ...
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35 views

Which ML algorithm is suitable for the following problem?

Assume i have x states each defined by an n-dimensional feature vector. In addition i have a set of actions which can be taken in each state resulting in a state action score. What would be an ...
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33 views

Proof that a density proportional to Gaussian is Gaussian [duplicate]

I try to develop Bayesian estimation for one dimensional Gaussian with unknown $\mu$ and known $\sigma$. I got \begin{align} p(x|D) &= \int p(x|\mu)p(\mu|D) d\mu \\ &=\int ...
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2answers
78 views

Detecting Bimodal Distribution

I have histograms of audio signals where they have bimodal "normal" distribution. What I want to do is to detect these subpopulations inorder to have a threshold, this is meant to divide the values ...
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What can I possibly get from my data to find what I need?

I am an end-user of a program. This program returns an output based on an input (a file + a database). I have made a few tests with differents files and databases, then constructed a CSV file of the ...
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1answer
294 views

Request: Clever Things to do with Naive Bayes

I am trying to drive up the performance of a Naive Bayes classifier, and I haven't been having terribly much luck. I've been working in Weka, but have enough knowledge of R to (possibly) implement ...
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23 views

Unbiased Estimator of Product

Suppose there are stationary times series $\{A_i\}_{i=1}^{T},\{B_i\}_{i=1}^{T},\{C_i\}_{i=1}^{T},\{D_i\}_{i=1}^{T}$, which may not necessarily be independent processes. We know that ...
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1answer
40 views

Starting to apply theoretical “data science” learnings to real-world data sets

I've been reading a number of data science books recently (Python, R, etc), plus attempting a number of the MOOC courses, and so on, and the content is reasonably consistent: regression, ...
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29 views

the Chi-squared distribution and Mahalanobis

I'm doing some simulations in C++ and OpenGL (2D and 3D) for navigating a robot in unknown environment known as SLAM. I'm using Extended Kalman filter as an optimal estimator. In SLAM problem, once ...
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1answer
37 views

Eigenfaces in R: How to reconstruct original features from principal components? [duplicate]

I've performed PCA on face images dataset and I'm not sure how can I use the most informative principal components to show the "reduced" image. The original image is 96*96 pixels (96*96 = 9216) and I ...
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26 views

Binary classifier via Mahalonobis distance

In a recent conversation with a colleague at univerity, they mentioned that for a certain problem, we can "just use a binary classifier". When I inquired as to how they would train, they said "No ...
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1answer
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Check a status of training process in R

I'm training a model using caret package in R for almost 3 days. The calculations are running in parallel (multiple processes). Unfortunately there is no output in ...
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2answers
68 views

Modelling Technique

I have a 5 years of Cargo insurance (goods transportation insurance) data. I need to predict the claim amount based on their policy date and some other variables like mode of transportation, Country ...
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21 views

How can i do time series forecasting with missing data

I am relatively new to time series forecasting, I have worked previously with continuous data at regular intervals successfully, Now I have a data set with missing values, for example look at the ...
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Is it possible for a reinforcement learning agent to create or generate additional features

I have little background knowledge of Machine Learning, so please forgive me if my question seems silly. Based on what I've read, the best model-free reinforcement learning algorithm to this date is ...
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2answers
45 views

forecasting sharp seasonal peak in time series

I have time series data on a daily level over the past 4 years. What is clear from examining past data is that there are two very clear peaks in the time series around the same time of year (they ...
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2answers
64 views

Generating recommendations using matrix multiplications

The Mahout In Action (Chapter 6) book contains a recommendation method based on matrix multiplication that uses co-occurrence data (C) in combination with user preferences (U) to generate user ...
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1answer
25 views

Dimension Reduction

I have a n*m matrix, the rank of matrix (r) is near to min(m,n) I want to minimize the rank by removing some of the rows or columns to get r << min(m,n) The goal is to achieve least rank for ...
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How many data do you need for a convolutional neural network?

If I have a convolutional neural network (CNN), which has about 1,000,000 parameters, how many training data is needed (assume I am doing stochastic gradient descent)? Is there any rule of thumb? ...