17 votes
Accepted

What is meant by the term "prior" in machine learning

Put simply, and without any mathematical symbols, prior means initial beliefs about an event in terms of probability distribution. You then set up an experiment and get some data, and then "update" ...
fade2black's user avatar
  • 9,837
11 votes
Accepted

Normalized measure from dynamic time warping

Sure. There's a straightforward way to convert an unnormalized distance metric into a normalized similarity measure. Basically, use $$S(x,y) = \frac{M - D(x,y)}{M},$$ where $D(x,y)$ is the ...
D.W.'s user avatar
  • 159k
4 votes
Accepted

What method of collective recogintion to use for digits recognition?

The state of the art for digit recognition does not use collective recognition, competence areas, ensembles, or any of the other ideas you propose in your question. Instead, the state of the art for ...
D.W.'s user avatar
  • 159k
4 votes
Accepted

What is a good approach to symbol identification/recognition given a path, instead of raster data

What you are looking for is called "on-line recognition". I have written my Bachelors thesis about this: Thoma, Martin. "On-line Recognition of Handwritten Mathematical Symbols." arXiv preprint ...
Martin Thoma's user avatar
  • 2,360
3 votes
Accepted

How does continuous computer vision works?

It depends on what you're recognizing, but I'm familiar with two basic techniques: Apply the recognition algorithm to each frame of the video. Be fast enough that you can run on every frame of video....
D.W.'s user avatar
  • 159k
3 votes

Machine Learning: Identify Patterns in Time-Series Data

Yes, your data is "time-series data", since it's a sequence of measurements of the same variable collected over time. Time-series data can be collected continuously or at discrete intervals. Your ...
rphv's user avatar
  • 1,624
3 votes
Accepted

Which algorithm for counting the occurrences of a certain pattern (spots) in an image?

The best way to get an orientation is to read a textbook on computer vision or image processing. There are lots of techniques known, and that's often the best way to get an introduction to a broad ...
D.W.'s user avatar
  • 159k
3 votes

Extracting features for texture classification

If it is important for you to get a high accuracy, then use a convolutional neural network (ConvNet). These ConvNets hold the state of the art for most visual recognition tasks. If your training set ...
pir's user avatar
  • 131
2 votes

Automated lip-reading: inferring what someone is saying, based upon video of them speaking

There is some recent work here: LipNet: Sentence-Level Lipreading. Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, and Nando de Freitas. They achieve 93% accuracy on a corpus of 3-second ...
D.W.'s user avatar
  • 159k
2 votes
Accepted

deterministic finite automaton for "does contain substring $w$" is of linear size

Consider the following NFA: the states are $q_0,\ldots,q_{|w|}$, there are transitions $q_{i-1} \to q_i$ on $w_i$, there are self-loops on $q_0,q_{|w|}$ (for all alphabet symbols), the initial state ...
Yuval Filmus's user avatar
2 votes
Accepted

Where I can I find Pre Trained CNN Datasets for Facial Emotion Recognition?

You are probably interested in IAPS, and want to read Analysis of Physiological Signals for Emotion Recognition Based on Support Vector Machine. But to be honest this classification is doomed in ...
Evil's user avatar
  • 9,455
2 votes
Accepted

how to identify patterns in program execution flows?

There's probably no existing turnkey solution that will do what you want, out of the box. I suspect this would be a research-level task, so you might want to start by searching the research ...
D.W.'s user avatar
  • 159k
2 votes
Accepted

Efficiently find smallest unique substring

You can try suffix array approach which find all suffix of a given string of length n in O(n) time. There are many algorithm to construct suffix array from a given input string. Look at complete ...
Chits's user avatar
  • 119
2 votes

What is meant by the term "prior" in machine learning

It's roughly any pre-training choices you encode into your model In machine learning a prior is, according to the book "Deep Learning" by Goodfellow, Bengio, and Courville, a probability ...
bob's user avatar
  • 121
2 votes
Accepted

Finding (and possibly extracting) source code in heterogenous text data set

There are many possible ways you might do this. I'll suggest one. I suggest you train a classifier to recognize whether a sequence of characters is code or text. Here, the goal is to build a ...
D.W.'s user avatar
  • 159k
2 votes
Accepted

reverse string to pattern matching

The Shortest Levenshtein's Distance should be good enough. See if this Matching Wildcards Wikipedia page can be of help.
Devesh Thakur's user avatar
2 votes

Recognizing a trajectory from a set

The Fréchet distance is a similarity measure for trajectories that often works well for curves that we consider visually similar. However, it is not scale or translation invariant, because it compares ...
Discrete lizard's user avatar
  • 8,248
2 votes

A pretrained model for mathematical equations characters detection

Detexify is a service that recognizes LaTeX symbols from handwritten figures. Their training dataset is freely available on Github.
Discrete lizard's user avatar
  • 8,248
2 votes
Accepted

Can computer vision process 3D “images” directly?

This is a very general question, so I'll give some ideas. The short answer is yes. If you're talking about the "processing" as some function, then yes, we can use some function $f_3: \mathbb{...
mikinty's user avatar
  • 352
1 vote
Accepted

Simultaneous regression of multiple curves in the same dataset or image?

There are many possible methods. In your picture, there aren't too many dots, so the following algorithm might be good enough: For each pair of points, find the line through them. Count the number ...
D.W.'s user avatar
  • 159k
1 vote
Accepted

Periodic Pattern recognition In a Sales Log

These are the steps you need to follow. Model You need to pick a model that you think explains your data. It can be very simple (e.g. linear regression) or very complex (e.g. a four-layer neural ...
Raphael's user avatar
  • 72.4k
1 vote

What kind of pattern recognition algorithm would Facebook use to detect suicidal users?

I would take march average to calculate several indicators from posts of the same user including but not limited to: - emotional-tagged words from posts (using available dictionary for this purpose) ...
Evil's user avatar
  • 9,455
1 vote

Computation of normalized first derivative in discrete case

As you have noticed, the denominators in the definition of $x'_t,y'_t$ ultimately have no effect, as they will be normalized away. Nonetheless, they do have significance. This definition ensures ...
D.W.'s user avatar
  • 159k
1 vote

Computation of normalized first derivative in discrete case

For the first part of your question, the significance is just to guarantee that the vector $(\hat{x}'_t,\hat{y}'_t)$ has length $1$. This means that it tells us in which direction $(x_t,y_t)$ is ...
David Richerby's user avatar
1 vote

Trying to detect recurring events at certain intervals, e.g. daily, weeekly, monthly, annually

Given an event type and a sequence of times at which it has occurred, there are several ways to check whether it seems to recur periodically. One simple approach is to look at the interarrival times (...
D.W.'s user avatar
  • 159k
1 vote

Efficiently find smallest unique substring

I can suggest a simple method that will achieve this in many practical cases in linear time, for example if you examine "War and Peace", but will fail if some characters are very common. Assume the ...
gnasher729's user avatar
1 vote

What is meant by the term "prior" in machine learning

In Bayesian statistics, a "prior" represents the beliefs we have before observing some data. Then, after we observe some data, we update our beliefs; those updated beliefs are called the &...
D.W.'s user avatar
  • 159k
1 vote

Changing Rabin-Karp without modulo to Rabin-Karp with modulo

Don't wait to do the modular reduction at the end. Every time you do an addition or multiplication, immediately reduce modulo the modulus. This keeps all intermediate results small.
D.W.'s user avatar
  • 159k
1 vote
Accepted

Simple Recognition Algorithm, How Does It Work?

Normally we build a classifier that accepts images of a fixed size: say, 100x100 pixels. Given an image that is larger than that, we re-scale it to 100x100 pixels before feeding it into the ...
D.W.'s user avatar
  • 159k
1 vote

Which algorithm for counting the occurrences of a certain pattern (spots) in an image?

First thing, you must be clear about the pattern because computer vision has still focused on pattern specific algorithms. If I consider your pattern is circularity if I am not wrong you are asking ...
Jay Patel's user avatar

Only top scored, non community-wiki answers of a minimum length are eligible