Properties (45)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
software
|
| gptkbp:basedOn |
Tsetlin automata
|
| gptkbp:competesWith |
deep learning models
|
| gptkbp:completed |
high accuracy
|
| gptkbp:electionYear |
real-time applications
|
| gptkbp:evaluates |
binary classification
|
| gptkbp:hasPrograms |
natural language processing
|
| https://www.w3.org/2000/01/rdf-schema#label |
Tsetlin machine
|
| gptkbp:influenced |
Markov processes
|
| gptkbp:inspiration |
game theory
|
| gptkbp:introduced |
2018
|
| gptkbp:is_a |
discrete model
|
| gptkbp:is_a_model_for |
learns from examples.
|
| gptkbp:is_a_platform_for |
learning from data
|
| gptkbp:is_a_resource_for |
large datasets
|
| gptkbp:is_a_subject_of |
computational learning theory
|
| gptkbp:is_a_time_for |
feature selection
pattern recognition |
| gptkbp:is_a_tool_for |
data mining
|
| gptkbp:is_characterized_by |
simple rules
|
| gptkbp:is_designed_to |
simplify model interpretation
|
| gptkbp:is_evaluated_by |
benchmark datasets
|
| gptkbp:is_known_for |
low computational cost
|
| gptkbp:is_part_of |
ensemble methods
the field of machine learning |
| gptkbp:is_recognized_for |
gptkb:C++
Python its simplicity |
| gptkbp:is_studied_in |
artificial intelligence
|
| gptkbp:is_used_in |
financial forecasting
academic papers classification tasks image classification |
| gptkbp:isFacilitatedBy |
noisy data
|
| gptkbp:isUsedFor |
other algorithms
|
| gptkbp:performance |
big data
|
| gptkbp:produces |
gptkb:Ole-Johan_Dahl
|
| gptkbp:provides |
interpretable models
|
| gptkbp:providesTrainingFor |
reinforcement learning
|
| gptkbp:related_to |
decision trees
|
| gptkbp:relatedTo |
support vector machines
|
| gptkbp:safety_features |
overfitting
|
| gptkbp:suitableFor |
online learning
medical diagnosis |
| gptkbp:utilizes |
Boolean features
|