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Results tagged with machine-learning
Search options not deleted user 609
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
2 votes
Prerequisites for Data Science
So far the answers have focused on learning particular methods. They are fine, but they won't make you a Data Scientist. Being a Data Scientist is not solely or even primarily about having mastery of …
- 1,840
3 votes
Does reinforcement learning only work on grid world?
Reinforcement learning does not depend on a grid world. It can be applied to any space of possibilities where there is a "fitness function" that maps between points in the space to a fitness metric. …
- 1,840
6 votes
Accepted
What is the term for when a model acts on the thing being modeled and thus changes the concept?
There are three terms from social science that apply to your situation: Reflexivity - refers to circular relationships between cause and effect. In particular, you could use the definition of the t …
- 1,840
2 votes
How to model this "un predicatability" problem?
tl;dr: This is not a great candidate for machine learning solutions. It is not exactly true that "the strength of an encryption scheme is measured by the randomness (unpredictability, or entropy) o …
- 1,840
31 votes
Accepted
How to generate synthetic dataset using machine learning model learnt with original dataset?
The general approach is to do traditional statistical analysis on your data set to define a multidimensional random process that will generate data with the same statistical characteristics. The virt …
- 1,840
0 votes
Looking for algebras designed to transform time series
The most direct and obvious transformation is from time domain to frequency domain. Possible methods include Fourier transform and wavelet transform. After the transform the signal is represented by …
- 1,840
4 votes
Accepted
Rough vs Fuzzy vs Granular Computing
"Granularity" refers to the resolution of the variables under analysis. If you are analyzing height of people, you could use course-grained variables that have only a few possible values -- e.g. "abo …
- 1,840
5 votes
Unstructured text classification
Topic Modeling would be a very appropriate method for your problem. Topic Models are a form of unsupervised learning/discovery, where a specified (or discovered) number of topics are defined by a lis …
- 1,840
2 votes
non query-based document ranking
You could use Topic Modeling as described in this paper: http://faculty.chicagobooth.edu/workshops/orgs-markets/pdf/KaplanSwordWin2014.pdf They performed Topic Modeling on abstracts of patents (limit …
- 1,840
1 vote
Distance calculation/vector range significance
I created a scoring system ("Thomas Scoring System") to deal with this problem. If you treat "distance" as a similarity score, this system should work for you. http://exploringpossibilityspace.blogsp …
- 1,840
2 votes
Accepted
R Script to generate random dataset in 2d space
None of the algorithms you mention are good with data that has uniform distribution. size <- 20 #length of random number vectors set.seed(1) x <- runif(size) # generate samples …
- 1,840
5 votes
Data driven approach to define a churn user
Another approach would be to model "churn" (aka "diminished use of the service, including non-use") as a process and not an event. Years ago in retention marketing this was called a "defection funnel …
- 1,840
6 votes
Accepted
Word2Vec for Named Entity Recognition
Instead of "recursive neural nets with back propagation" you might consider the approach used by Frantzi, et. al. at National Centre for Text Mining (NaCTeM) at University of Manchester for Termine (s …
- 1,840