Learning active instances on the border in the case of imbalanced data classification task.
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Updated
May 27, 2024 - Python
Learning active instances on the border in the case of imbalanced data classification task.
Implementation of Beyond Neural Scaling beating power laws for deep models and prototype-based models
End-to-end implementation of Malaria detection using deep-cnn prior feature map extractor plus svm, rf, xgboost, rslvq and celvq with options for soft and hard ensemble for the prototype-based models using prosemble ML package
Geographic Information System-based decision support system using the Learning Vector Quantization method.
code for the paper Beyond Neural scaling laws for fast proven robust certification of nearest prototype classifiers
A python project for prototype-based machine learning models
A python project for prototype-based soft feature selection
A python project for prototype-based feature selection
Prototype-Based Soft Feature Selection Package
Implementation of Learning Vector Quantization (LVQ) and Extreme Learning Machine (ELM) with Iris Dataset
🧠 Java Machine-Learning framework for model training, evaluation, deployment, tuning and benchmarking!
ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.
Prototype-based Feature selection with the Nafes Package
Code for the paper Mutation Validation for Learning Vector Quantization.
Learning Vector Quantization ( or LVQ ) is a type of Artificial Neural Network which also inspired by biological models of neural systems. It is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm similar to Self Organizing Map.
ProtoFlow is a TensorFlow-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.
Building a Learning Vector Quantization 1 (LVQ1) network
NeuPy is a Tensorflow based python library for prototyping and building neural networks
Prototype based ML implementation for Multiple reject thresholds for improving classification reliability
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