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[ti]ny [li]ttle machine learning [tool]box - Machine learning, anomaly detection, one-class classification, and structured output prediction

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tilitools

Travis-CI

tiny little machine learning toolbox

Tilitools is a collection of (non-mainstream) machine learning model and tools with a special focus on anomaly detection, one-class learning, and structured data. Author: Nico Goernitz

Examples, description, and lectures examples can be found in the notebooks/ directory.

Test data was collected from ODDS (outlier detection database).

Currently available models:

  • Bayesian data description
  • support vector data description:
    1. dual qp
    2. latent
    3. cluster
    4. multiple kernel learning
  • one-class support vector machine:
    1. huber loss primal
    2. latent
    3. primal lp-norm w/ SGD solver
    4. dual qp
    5. convex semi-supervised
    6. multiple kernel learning
  • lp-norm mkl wrapper
  • structured output support vector machine (primal)
  • latent variable principle components analysis
  • LODA

structured objects:

  • multi-class
  • hidden markov model

supported kernels and features:

  • rbf, linear kernel
  • histogram intersection kernel
  • histogram features
  • hog features

lectures: Lectures contains exercise and solution notebooks for various topics. Right now the following are available:

  • introduction to anomaly detection
  • optimization I + II
  • learning with kernels I + II
  • geoscience project grainstones: detecting fossils in microscopic images

notebooks: Contains notebooks to various topics related to machine learning, anomaly detection, and structured output learning. Currently, the following are available:

  • high-dimensional outlier detection
  • the anomaly detection setting
  • Bayesian data description
  • introduction to SVDD and OCSVM

data: The data sub-directory contains well-known benchmark datasets which are modified to fit in the anomaly detection setting. These modified datasets can be downloaded from ODDS website (outlier detection database).

installation: There are two packages that need to be installed separately

  • pyOD (for LODA)
  • pytorch / torchvision

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[ti]ny [li]ttle machine learning [tool]box - Machine learning, anomaly detection, one-class classification, and structured output prediction

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