Questions tagged [pattern-recognition]

Refers to techniques for classifying data into categories based on similarities (which can either be known previously, or learned).

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Explain equation 1.80 in Pattern Recognition and Machine Learning, Bishop

$$E[L] = \sum_k \sum_j \int_{R_j} L_{k,j} p(x, C_k)$$ L is a loss function that returns a real value given a pair (i,j), with i as the index of true class, and j as the index of the predicted class of ...
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How to find patterns in large panel data set over some years to predict a variable in other years

I have a big panel data set (more than 500 million observations) which contains information on individuals who did or did not receive treatment in certain years (2000-2010). For the years before that (...
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Can we generate online handwriting recognition dataset by algorithms

For online handwriting recognition (or even wider machine learning tasks), is it possible to get large dataset for learning by generating handwriting sentences from the combination of some individual ...
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42 views

Viewing a kernel as a probability density: Why are we only integrating with respect to $x$ and not $x^\prime$?

I am currently studying the textbook Learning with kernels: support vector machines, regularization, optimization and beyond by Schölkopf and Smola. Chapter 1.2 A Simple Pattern Recognition Algorithm ...
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68 views

Q-function in Q-Learning

I ran into solved old-exam question as follows: My notes tell me that option b is correct but I think option d is correct. is there any idea why (b) is correct?
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Information gain of the root node

Recently I saw this question and answer as attached in following image Anyone can add details how this solution achieved?
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taylor series expansion in laplace approximation of bayesian neural network prediction

In chapter 6 of the book Pattern recognition and machine learning, there is this part about prediction in Bayesian neural network using laplace approximation : why assuming small variance compared ...
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Probability distribution of training set t in Bishop's pattern recognition book

I'm currently struggling with understanding the Bayesian approach to machine learning. Which is one of the paradigms presented in Bishop Pattern Recognition and Machine Learning. Since some parameters ...
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Chernoff bound for bayes classifier

It's mentioned in many pattern recognition textbooks (Duda, Theodoridis,etc) that Chernoff distance is: but I couldn't find the proof and I wasn't able to derive it myself. Some insight on the ...
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25 views

Why might the functional form of a distribution be “inappropriate” for a particular application?

Working through Bishop's Pattern Recognition and Machine Learning(a great read so far!) and on page 67 he says: "One limitation of the parametric approach is that it assumes a specific ...
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How to find 95% CI of a matrix of classification data?

I am running some support vector machine (SVM) analysis. I can run the analysis and even plot the obtained hyperplane, with methods similar to what is reported here. Essentially, I create a large ...
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How to improve facial recognition using cosine similarity

I'm using pretrained vgg16 model for feature extraction and then using cosine similarity to compare 2 embedding more like Siamese network. It gives descent results, above 60% for the true match and ...
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Why maximizing the expected value of log likelihood under the posterior distribution of latent variables maximize the observed data log-likelihood?

I am trying to understand the Expectation-Maximization algorithm and I am not able to get the intuition of a particular step. I am able to verify the mathematical derivation but I want to understand ...
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Euclidian distance vs cosine similarity

Currently I'm working on facial recognition. If I use encoding/feature vectors of 2 images which method will prove more accuracy, L2 norm or cosine similarity and why? I read "ICA performs ...
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How can you use a neural network to extract the needed information from social media ads?

How to solve the following problem using neural networks / and machine learning / artificial intelligence? Input data - is an ad from a channel or group of a social network. For example, this: A room ...
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What is the statistical relevance of gamma in k-prototypes algorithm and why is it related to the standard deviation of the numeric columns?

The k-prototype algorithm uses gamma to provide weight to the categorical features. I have a few queries regarding it : Why is there no upper limit to it? Should it not be (1-gamma) such that gamma ...
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Question about package 'dtw' in R [closed]

Please see below images. Using package 'dtw' in R, is there a way to ensure that pattern 1) has a lower computed distance relative to the reference pattern than pattern 2) does. In other words, can ...
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How can I determine a gaussian field for thresholding a distribution at each location of a grid?

Suppose $X(u, v) \in R$ is a random variable at location $(u,v)$ of a grid $G \subset R^2$, and $X(u,v)$ can be expectedly decomposed into two components with a unknown threshold $t(u,v)$ with $t(u,v) ...
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What is the best structure (Accuracy of the text extracted) for building an OCR? ATTENTION, CRNNN, DRAM,RAM, CTC based

If I want to make a new OCR for extracting text from textbooks, specially maths and chemistry, what should be the structure for the OCR? THERE ARE LOT OF TUTORIALS around the internet but no one ...
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Predicting Rare Events (Incidents) from Sequence Data: Using RNNs

I have a problem I am interested in, and I am thinking of it in the context a neural network sequence model (any appropriate variation of RNN) BUT please correct me if another model is more apt: Data:...
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Help me find a good correlation test for a set of mixed data

I'm currently stuck in finding the right test to do for a set of mixed data. I'll briefly explain what it is about. I would like to find patterns in the parameters that are measured for 5 airlines. I'...
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Integration factors in Variational Bayes' predictive distribution

Under the Variational Bayes framework, a posterior distribution $p({\bf Z}|{\bf X})$ for latent variables ${\bf Z}$ and observed variables ${\bf X}$ is approximated by finding the distribution $q$ ...
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Heart rate vs sleep quality ( need advise about statistical strategy for better compare)

I LIKE TO KNOW ABOUT how to compare result of some detected patters in heart rate for sleep quality,for something like this work: Image-based sleep motion and Slope detection on Hammock, hill and ...
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Logistic variational lower bound with hyperparameters

I am studying from Christopher Bishop's Pattern Recognition and Machine Learning book chapter 10. In section 10.6.3 he derives a variational approach to approximate the joint distribution $$ p({\bf w}...
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Analysis to view patterns or correlations between multiple variables

I have a dataset with many variables (yes/no) questions from a questionnaire on different symptoms experienced (yes I have symptom/no I don't have symptom) with thousands of participants. As an ...
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Where can I find pattern recognition lecture videos available on the web (for biomedical engineering students)?

I have seen a previous question on this on Quora: What are the best pattern recognition lecture videos available on web? However, it was asked in 2015. I was wondering if there are any more current,...
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LDA vs QDA on the AT&T dataset, poor QDA performance

I am obtaining two very different accuracies for the AT&T face database when fitting the model with lda & qda. Before using QDA I first search for the ideal regularisation parameter, AFAIK the ...
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93 views

Mahalanobis distance between high dimensional arrays

As we know, the Mahalanobis distance (MD) is one of the distance metrics for measuring two points in multivariate space. In practice, I can compute Mahalanobis distance between two 1D arrays using ...
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Named entity recognition with only one pure entity(no context)?

We know that we can extract entities from a sentence using named entity recognition, but what if the sentence contains only an entity and no other context? For example, we can use CRF for the ...
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How Parzen window density estimate $f_n$ converges to f

I am trying to understand how Parzen window density estimate converges to actual density function f(x).[Actually i am trying to learn machine learning on my own using available free resources. Please ...
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Doubt in kernel based method - unit hypercube(Parzan window estimate)

I recently started studying pattern recognition on my own. Please clarify me the following. https://books.google.co.in/books?id=T0S0BgAAQBAJ&pg=PA53&lpg=PA53&dq=hypercube+of+side+h&...
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Doubt in bayes classifier error calculation

I have recently started machine learning on my own. I started reading Duda art and start book. That author says that Bayes classifier has a min error. He calculates $$\begin{equation} P(error|x)=\...
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Updating the Decision Surfaces of a trained LDA classifier for Face Recognition

I want to perform an experiment where I compare two classifiers based on LDA: The first classifier is trained on all images of one half of the total amount of persons in the data set The second ...
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Does the shape of a pattern influence the performance

Given I have a feature set with around 6 features. These features represent the shape of a pattern. Each feature is a point in two dimensional space. Hence, an observation looks like: $$(x_1, y_1), (...
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How can I find patterns in a histogram?

I'm into image classification and I'm using Local Binary Pattern Histogram(LBPH) method. Works great! Only bad thing is that it cannot handle complex pictures, like if the background changes. The walk ...
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Is there a way to know what features a Multinomial Naive Bayes classifier is taking into account to predict categories?

I have created a Multinomial Naive Bayes classifier. The dataset looks like this: ...
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Identifying original elements in a permutation of a pattern

In the pattern above, the blue peaks are known and are labeled $p_1, p_2, ... p_N$. The red line is the same pattern of peaks, but altered slightly. Given a blue pattern and red pattern, I want to be ...
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How to use the likelihood-ratio to compute the error probability? [closed]

In Bayesian decision theory, There is an analytical form of error rate, which is $$P(e)=\int P(e|\bf{x})p(\bf{x})d\bf{x}$$. For binary classification, we can compute the type I error probability with: ...
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DTW find the warped sequences

I have these 2 sequences: s1 = [1, 2, 3, 5, 5, 5, 6] s2 = [1, 1, 2, 2, 3, 5] And have calculated the DTW matrix as: ...
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Is there a good measure of “paternless-ness” in a set of data?

This may be a slightly open-ended question, or even a bad one, but are there any measures of looking at a set of data and seeing whether there is any kind of pattern or not? For some context my ...
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484 views

Why is pattern recognition often defined as an ill-posed problem?

A Well-posed problem should have the following properties: a solution exists the solution is unique the solution's behavior changes continuously with the initial conditions. A problem which is not ...
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Algorithm for time-domain pattern matching

I have the following problem: I have a hypothesized pattern of a sensor that will most likely fall into four categories A, B, C, or D. Each sample reading contains the same amount of datapoints. My ...
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Algorithms or techniques for pattern recognition [closed]

I am looking for algorithms or techniques that could be used for pattern recognition/extraction in data. Preferably I would like to know if there are any algorithm implementations in Python that I ...
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How should I proceed to classify images of not ok microchips

So I have a new project where I have to classify different types of damages on microchips. I am new to machine learning and python in general so I am a little bit lost. I have over 100.000 images I ...
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Timeseries Pattern recognition

I have about 8000 timeseries from kepler. Every timeseries is from another star. Every timeseries are classified into two classes (there is planet orbitting around star, there is no planet). X axis ...
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Computing the error function defined over a transformed dataset

I am currently reading chapter 5 from Christopher Bishop's Pattern Recognition and Machine Learning book. Having an infinte dataset $\{({\bf x}, t)\}$, and an estimated value $y({\bf x})$, I am ...
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Ways to extract patterns yielding high scores

Suppose I have a table, containing several features and a score denoting the performance (higher is better) of the corresponding features. Like this: ...
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Which distance metric to use to cluster categorical sequences (clickstreams or clickpaths)?

For my research, I want to cluster website visitors based on their clickstreams to understand different information behavior patterns (i.e., customer/visitor journeys). The data can be characterized ...
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Practical Examples: Expectation of a function with respect to a probability

I have encountered the following phrasing while reading Bishop's "Pattern Recognition and Machine Learning": Although for some applications the posterior distribution over unobserved variables will ...
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Sampling Methods : Pattern Recognition and Machine Learning Bishop

I am reading chapter 11 . Sampling Methods from the book : Pattern Recognition and Machine Learning by Bishop : In the introduction , in short,he evaluates expectation of some function $f(z)$ with ...

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