MLDemos is an open-source visualization tool for machine learning algorithms created to help studying and understanding how several algorithms function and how their parameters affect and modify the results in problems of classification, regression, clustering, dimensionality reduction, dynamical systems and reward maximization.
Created by Dr. Basilio Noris at the Learning Algorithms and Systems Laboratory, the development of this program has been aided, supported and sponsored by the following entities, organizations, and groups
Implemented Methods
Classification- Support Vector Machine (SVM) (C, nu, Pegasos)
- Relevance Vector Machine (RVM)
- Gaussian Mixture Models (GMM)
- Multi-Layer Perceptron + BackPropagation
- Gentle AdaBoost + Naive Bayes
- Approximate K-Nearest Neighbors (KNN)
- Gaussian Process Classification (GP)
- Random Forests
- Support Vector Regression (SVR)
- Relevance Vector Regression (RVR)
- Gaussian Mixture Regression (GMR)
- MLP + BackProp
- Approximate KNN
- Gaussian Process Regression (GPR)
- Sparse Optimized Gaussian Processes (SOGP)
- Locally Weighed Scatterplot Smoothing (LOWESS)
- Locally Weighed Projection Regression (LWPR)
- GMM+GMR
- LWPR
- SVR
- SEDS
- SOGP (Slow!)
- MLP
- KNN
- Augmented-SVM (ASVM)
- K-Means
- Soft K-Means
- Kernel K-Means
- K-Means++
- GMM
- One Class SVM
- FLAME
- DBSCAN
- Principal Component Analysis (PCA)
- Kernel PCA
- Independent Component Analysis (ICA)
- Canonical Correlation Analysis (CCA)
- Linear Discriminant Analysis (LDA)
- Fisher Linear Discriminant
- EigenFaces to 2D (using PCA)
- Random Search
- Random Walk
- PoWER
- Genetic Algorithms (GA)
- Particle Swarm Optimization
- Particle Filters
- Donut
- Gradient-Free Methods (nlopt)
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