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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Identifying and describing patterns of change over time

I have a data set of patients who suffer from a certain terminal illness. Some time after the onset of the disease, a specific vital function parameter starts to decline until it is no longer ...
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Software to identify or reverse engineer patterns from encoded / decoded data

I have a CSV file that contains encoded date data. I have no idea how the data was encoded, but you can look at the data and see an obvious pattern which means it was probably some sort of custom ...
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Distance between two clusters after their joining in centroid linkage

For a distance between two clusters A and B of objects given by $d_{AB}=\left \|{m_{A}-m_{B}} \right \|^{2}$ , where $m_{A}$ is the mean of the objects in cluster $A$, show that the formula ...
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Identification of a pattern based on given features in time-series data

I am working with electricity time-series data collected at 15 minutes intervals. I am looking for a procedure/theory to find the pattern/sequence in the time-series data based on given features. As I ...
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Flagging bad time series behavior (Pattern Recognition and Outlier Detection)

I want to get some opinions on how to approach the following problem to do with detecting "unhealthy" behavior in time series data (either using a statistical/analytical model or ML/DL, I do ...
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Measure how dataset is harmonious or organized

Suppose we have two set of numbers : A = [1,4,9,16,25,49...100] and B = [1,4,7,7,25,49,64...100]. As you seen the first one is consistently growing, elements of it is square of numbers. But although ...
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What is the difference between these two types of training?

Suppose that I want to detect if a picture contains a particular logo, for instance the following one. Since template matching would be slow and fail those scaled or resized ones, I decided to train ...
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Discover a pattern in a set of spectrograms given a real rating

I'm trying to find patterns in a sorted set of spectrograms, each with a rating. The main idea is to train the CNN in such a way that it understands how the pattern "evolves" at each step, ...
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PCA to identify patterns in the data, forced to a particular variable?

Dataset: I have a hyperspectral dataset that consists 250 wavelength bands (x1,...x250) and corresponding reflectance measurements (y) for each band. Plotting X vs Y yields a spectral profile. I have ...
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How does addition of a regularization term ensures that the matrix is nonsingular? ( least squares )

In Bishop's Pattern recognition book, in 3.1.2 Geometry of least squares section (page 143, last paragraph of section), it is stated that: In practice, a direct solution of the normal equations can ...
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Do you know any video course based on Pattern Recognition and Machine Learning book of Bishop? (I gave one advice)

I am reading the Pattern Recognition and Machine Learning (PRML) book of Bishop. For a better understanding I look for video series on youtube. I found one (if you need): this I would like to know if ...
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Clarification of line in proof of consistency theorem (Vapnik)

In Vapnik's Statistical Learning Theory (1998 edition) on pages 89-92, he proves a "key theorem of learning theory" that states the conditions for when: "the following two statements ...
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Intuition behind random strides in CNNs

I recently attended a lecture on CNNs and was given a brief overview on the topic of dropout. I understood the logic behind the regularization and silencing the firing of neurons to prevent ...
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what we can use instead of mean of class in fisher’s linear discriminant?

I Need to know can we use another soulation to calculate bias that is center of mean for each class(1,2) ??? actually i need to use another way for bias in fisher’s linear discriminant instead of ...
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Difference between dynamic time warping, windowed cross-correlation and wavelet coherence?

I have video data with people talking with each other and I'm planning to analyse patterns of their head movements and facial expressions. I'm not very advanced in terms of statistics, so could you ...
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A reference on statistical pattern recognition with solution manual

I'm taking a graduate course on statistical pattern recognition in the upcoming semester. I was wondering if there's a reference book with a worked solution manual, to make learning the course easier? ...
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Bayesian Parameter estimation (Pattern Classification by Duda, et al

I have been trying to solve question 17 of chapter 3 (Maximum Likelihood and bayesian estimation) of the book "Pattern Classification" by Duda, et al. The question goes as follows: Now the ...
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Machine Learning technique for strings as input and integers as output

I have some string that follow the format; {alphabet}-{some 3-5 digit number} For example, 'A-12345'. Those strings have a corresponding integer. And I want to predict that number. Thus my input space ...
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Siamese Network for face comparison wont learn, accuracy stuck on 0.5, and loss stuck too [duplicate]

I'm trying to train a siamese network which contains a CNN and an embedding layer at the end to yield 2 similar (close) vectors for 2 images of the same person. I'm using the LFW_Cropped dataset, and ...
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2 votes
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Robustly extracting subpatterns from time-series data

As part of an experiment, I need to collect time-series samples which are tightly associated with some input data. I send this data to an external device, and then collect the associated trace using ...
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What does the meaning of the autocorrelation in this picture?

From this figure, how should I understand what is the lag on the top figure? and when in the bottom figure for example the autocorrelation is 0.45 what does tell us about the above figure? Another ...
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If machine learning, in general, is about ‘learning’ patterns, is it correct to say its branches differ based on the type of pattern being learnt?

If machine learning, in general, is about ‘learning’ patterns, is it correct to say its branches differ based on the type of pattern being learnt? I.e. in supervised learning the pattern is a target ...
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How to extract simple shapes from a feature map?

I am working on image parsing project. I want to find a way to automatically parse an object into a list or a graph of simpler shapes. Is there any practical information on how to do so? So far I took ...
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Support Vector machine suitable for gesture recogniton EMG?

I have to choose a suitable algorithm for prosthesis control with an 8-electrode array. I am analyzing and processing the EMG data with R. I wanted to try to do it with SVM, but the results seem quiet ...
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Cluster analysis with interchangeable, binary classes

The Data: I have the results of an experiment where participants were given 30 stimuli and asked to sort them into two groups. The participants were asked to sort them into the two groups without any ...
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Avoiding overfitting in unsupervised ML

I am using a unsupervised pattern matching approach to create a trade strategy. I use the output of the pattern matched results to decide whether to enter a trade or not. For deciding the best pattern ...
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Techniques for finding patterns in binary data

I have a question about data analysis and I am wondering if someone can advise? I am interested in what mathematical techniques are available to look for patterns in binary data. Say I have a black ...
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Comparing probabilites of error for differenct 'k' in a k-NN Classifier

Here is a question that I'm stuck with: Consider two classes $w_1$, $w_2$ in the two-dimensional space. The data from class $w_1$ are uniformly distributed inside a circle of radius $r$. The data of ...
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2 votes
1 answer
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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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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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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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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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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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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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1 answer
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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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1 vote
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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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3 votes
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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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2 votes
1 answer
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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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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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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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