10
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Data Science vs Operations Research
While both Operations Research and Data Science both cover a large amount of topics and areas, I'll try to give my perspective on what I see as the most representative and mainstream parts of each.
...
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10
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any hope for a universal automatic parser?
You might be interested in learning about grammar induction: given a set of examples of strings from a context-free language, there are algorithms to learn a context-free grammar that generates those ...
D.W.♦
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6
votes
Detecting events in time series data
Your first step is to characterize what effect you expect an event to have on your signals. Does it change the mean? Increase the mean? Change the variability? The more you can say about the type ...
D.W.♦
- 156k
6
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Data Science vs Operations Research
This isn't a full answer, since mhum's is quite good in contrasting the differing aims of OR vs DS.
Rather, I want to address this comment of yours:
I was wondering if, for example, one could use ...
- 261
6
votes
What kind of logarithm does $\log$ mean in Wikipedia articles?
It depends entirely on context. In mathematics as a whole, $\log$ usually denotes the natural logarithm (base $\mathrm{e}$). In computer science, the situation isn't as clean because we often ...
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4
votes
Accepted
Computer vision: object detection with labels that are single coordinates
The state of the art in such problems is done these days via deep neural networks. Among others, two popular and recent approaches for solving the problem of detection and localization of objects are ...
- 398
3
votes
Measuring the information of a document?
Assuming that your data comes from a Markovian source, you can estimate the entropy of the source using an optimal compression algorithm such as Lempel–Ziv, whose theoretical version (without limiting ...
- 275k
3
votes
Gramatical evolution doesn't result on creating good constants
In Grammatical Evolution there are three well known approaches to the problem of constant creation:
expression based (the "traditional" approach). This is what you're using (arithmetic operators are ...
- 2,007
3
votes
Gramatical evolution doesn't result on creating good constants
It sounds like you're saying that your approach is able to effectively find the shape of the expression, but it takes it a lot longer to find the right constants.
One possible approach is to generate ...
D.W.♦
- 156k
3
votes
Word Frequency with Ordering in O(n) Complexity
The gathering of occurrence counts is O(n), so the trick is really only finding the top k occurrence counts.
A heap is a common way to aggregate the top k values, although other methods can be used (...
- 1,274
3
votes
How to generate response variable during machine learning?
No. You can't. You need ground truth. You're asking "if I don't know which claims are fraudulent, can an algorithm somehow determine that for me?" The answer of course is no: the algorithm doesn't ...
D.W.♦
- 156k
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 ...
- 1,604
3
votes
Accepted
In Data Mining, what does it mean to be greedy?
I'm not a Data Mining expert, but from what I understand, it means the same thing as it does outside of data mining: to improving the solution in a locally optimal way, rather than one that is ...
- 29.7k
3
votes
Data Science vs Operations Research
As a strategist, I've had the opportunity to work with both sides of the discipline. In trying to explain what OR and DS are to a qualitative MBA executive, my (overly) simplistic one line ...
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3
votes
Data Searching from a large data set without reading each element
Say you have $n$ entries, $m$ distinct letters, and the $i$-th letter occurs $k_i$ times. You can find the last occurrence of the $i$-th letter using a variant of binary search: Double your step size ...
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3
votes
Accepted
Why does the BFR (Bradley, Fayyad and Reina) algorithm assume clusters to be normally distributed around its centroid?
Roughly, the algorithm needs to estimate the probability to assign a point the correct cluster.
So the algorithm add P to a cluster if it is very unlikely that, after all the points have been ...
- 146
3
votes
Why is it not always possible to compute the centroid of feature vectors?
A metric space consists of a set $X$ of "points" and a metric $d\colon X \times X \to \mathbb{R}_{\geq 0}$ (giving the "distance" between any two points) which satisfies the following constraints:
...
- 275k
2
votes
Accepted
Standardizing Data for Neural Networks
You can distinguish between the following types of data (source, see Level of measurement for something similar):
Nominal: You can't calculate with them. They only support checks for identity, e.g. ...
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2
votes
Accepted
Exhaustive list of ways to distribute n objects to k sets
The objects you are interested in enumerating are counted by Stirling numbers of the second kind. In particular, $\genfrac{\{}{\}}{0pt}{}{n}{k}$ is the number of ways to partition a set of size $n$ ...
- 275k
2
votes
Accepted
Algorithm to find pronounciation rules
Broadly, I can see two possible approaches: machine learning, or data mining
Machine learning
You could look into using machine learning to learn a transducer that transforms the input sequence (the ...
D.W.♦
- 156k
2
votes
What to do when the information gain on decision trees is 0 for all possible splits?
You should process ALL of the possible splits and hope for information gain somewhere down the line.
- 121
2
votes
Accepted
Expected number of common edges for a given tree with any other tree
Let $T$ be your reference tree (on $n$ vertices), and let $R$ be the random tree. Label the edges of the tree randomly from $1$ to $n-1$. Let $X_i$ denote the event that the $i$th edge of $R$ is an ...
- 275k
2
votes
Accepted
About the MNIST data-set
The short answer is: I don't know.
Visualizations
There's been lots of work on visualizing the MNIST data set, in 2 dimensions, and even in 3 dimensions.
For instance, here's an embedding into 2D ...
D.W.♦
- 156k
2
votes
Relation and difference between information retrieval and information extraction?
From a modeling standpoint, information retrieval is a deep field predicated on several disciplines, including statistics, math, linguistics, artificial intelligence and now data science. In practice, ...
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2
votes
Machine Learning - Support Vector Machines
In addition to listed references, I would recommend the following:
A. Nefedov. Support Vector Machines: A Simple Tutorial, 2016
N. Cristianini, J. Shawe-Taylor. An Introduction to Support Vector ...
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2
votes
How to generate response variable during machine learning?
Let me know of any other methods to generate response variable when there is none
Anomaly detection techniques could be what you're looking for (but you don't "generate response variable when there ...
- 2,007
2
votes
A decision algorithm to choose what partner to use
Techniques developed for the multi-armed bandit problem might be useful for discovering the partner with the best acceptance rate and dealing with the changing acceptance rate over time. Those ...
D.W.♦
- 156k
2
votes
Data Science vs Operations Research
I obtained my Operations Research degree from a military institution and have been using it for military applications for about 15 years. In that timeframe, Data Science has grown into a main-stream ...
- 21
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.♦
- 156k
2
votes
Accepted
What kind of logarithm does $\log$ mean in Wikipedia articles?
I think it depends on the situation. For a mathematician, $\log n$ probably means the natural logarithm. For a computer scientist, $\log n$ probably denotes the base $2$ logarithm, etc.
I think ...
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