Pinned Repositories
aco-2dhp
Implementation of the Ant Colony Optimization meta-heuristic for the Protein Structure Prediction problem using 2D HP model. This approach includes the pull move heuristic for local search.
arm-stream-framework
ARM-Stream: Active Recovery of Miscategorizations in Clustering-Based Data Stream Classifiers
athena-client
birch
C implementation of the BIRCH algorithm, an unsupervised data mining algorithm which is used to perform hierarchical clustering over particularly large data-sets.
cdsc-al
A Clustering-based Data Stream Classification framework using Active Learning
data-stream-env
Strategies based on active learning and semi supervised learning for classification, novelty detection and concept drift detection over data streams.
minas
PCF compatible MINAS (MultI-class learNing Algorithm for data Streams). An algorithm to address novelty detection in data streams multi-class problems.
minas-reference-implementation
Reference implementation for MINAS (MultI-class learNing Algorithm for data Streams), an algorithm to address novelty detection in data streams multi-class problems.
pso-2dhp
Implementation of the Particle Swarm Optiomization algorithm for the Protein Structure Prediction problem using 2D HP model. This approach includes the pull move heuristic for local search.
sam-knn
A classifier for heterogeneous concept drift inspired in the biologically memory model.
douglas444's Repositories
douglas444/birch
C implementation of the BIRCH algorithm, an unsupervised data mining algorithm which is used to perform hierarchical clustering over particularly large data-sets.
douglas444/minas-reference-implementation
Reference implementation for MINAS (MultI-class learNing Algorithm for data Streams), an algorithm to address novelty detection in data streams multi-class problems.
douglas444/minas
PCF compatible MINAS (MultI-class learNing Algorithm for data Streams). An algorithm to address novelty detection in data streams multi-class problems.
douglas444/aco-2dhp
Implementation of the Ant Colony Optimization meta-heuristic for the Protein Structure Prediction problem using 2D HP model. This approach includes the pull move heuristic for local search.
douglas444/athena-client
douglas444/cdsc-al
A Clustering-based Data Stream Classification framework using Active Learning
douglas444/data-stream-env
Strategies based on active learning and semi supervised learning for classification, novelty detection and concept drift detection over data streams.
douglas444/pcf
Pattern Categorization Framework
douglas444/spring-data-query-examples
douglas444/streams
douglas444/arm-stream-framework
ARM-Stream: Active Recovery of Miscategorizations in Clustering-Based Data Stream Classifiers
douglas444/pso-2dhp
Implementation of the Particle Swarm Optiomization algorithm for the Protein Structure Prediction problem using 2D HP model. This approach includes the pull move heuristic for local search.
douglas444/2dhp-plot
This program can be used to plot the structure of a protein in the 2D HP model.
douglas444/AnyNovel
This is an implementation of AnyNovel Algorithm for detetcing novel concepts in evloving data streams. Paper can be found here https://link.springer.com/article/10.1007/s12530-016-9147-7
douglas444/arm-cdsc-al
douglas444/asterisk-jtapi
CVS repository from https://sourceforge.net/p/asterisk-jtapi/code/
douglas444/download-using-streaming-response-body
An example for streaming large files in chunks using StreamingResponseBody in Spring MVC
douglas444/ds-framework
Framework for data stream classifier algorithms
douglas444/echo
Semi-supervised framework for classifying evolving data streams
douglas444/echo-original
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.
douglas444/Higia
data streams, incremental clustering, novelty detection, concept drift
douglas444/jbirch
Automatically exported from code.google.com/p/jbirch
douglas444/mltk
Machine learning toolkit
douglas444/novelty-detection-env
douglas444/oidc-mock-example
douglas444/pcf-categorizers
douglas444/pcf-workspace
douglas444/pdf-with-html-template
With dynamic fields
douglas444/pso-sdl
Particle Swarm Optimization Graphical Simulatio
douglas444/quarkus
Quarkus: Supersonic Subatomic Java.