Priv.-Doz. Dr. Stefan Bosse
University of Koblenz-Landau, Fac. Computer Science
University of Bremen, Dept. Mathematics & Informatics 18.5.2018
sbosse@uni-bremen.de |
In social science big data volumes must be handled.
But big do not mean helpful or important!
Data is noisy and uncertain!?
F(Input Data): Input Data → Output Data
⇔
F(Sensor Data): Sensor Data → Knowledge
Such a function F performs Feature Extraction
But often there are no or only partial numerical/mathematical models that can implement F!
Usage of Artificial Intelligence and their methods can be helpful to derive such fundamental mapping functions - or at least an approximation: Hypothesis
The input data is characterized commonly by a high dimensionality consisting of a vector of variables
[x_{1},x_{2},..,x_{n}],
[y_{1},y_{2},..,y_{m}]
F: R^{N} → R^{M} with M ≪ N
f(x):x → y.
Machine Learning (ML) can be used to derive such relation from experimental/empirical training data!
Among the derivation of such functional relations the prediction of what will happen next or in the future is an important task of Machine Learning
Patient Details [weight,age,sex,pain left, pain right, temperature, ..]
Diagnosis Label {Appendicitis, Dyspepsia, Unknown, .. }
Returns one of the labels matching a new input vector x (the test object)
Decision classifiers only return one (good or bad) matching label
No information about matching probability