C++ Implementation of the RBF (Radial Basis Function) Network and choosing centroids using K-Means++
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Updated
May 9, 2015 - C++
C++ Implementation of the RBF (Radial Basis Function) Network and choosing centroids using K-Means++
cat🐈: the repo for the paper "Embarrassingly Simple Unsupervised Aspect extraction"
ProtVec can be used in protein interaction predictions, structure prediction, and protein data visualization.
kernalized t-Distributed Stochastic Neighbor Embedding (t-SNE)
I apply machine learning (ML) techniques to Snowplow web event data to understand how variation in marketing site experiences might correlate to customer conversion.
Application shows advantage of Classical MRAC using RBFs over PD control when unmodeled dynamics are present in the system (wing rock model).
Regularized Logistic Regression using mini-batch Stochastic Gradient Descent
MATLAB implementations of different learning methods for Radial Basis Functions (RBF)
To deal with non-linearly separable we use SVM's Kernel Trick which maps data to higher dimension!
SPPU - BE ENTC (2015 Pattern) - Elective III
GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits ‘4’ and ‘9’. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
This code reads a dataset i.e, "Heart.csv". Preprocessing of dataset is done and we divide the dataset into training and testing datasets. Linear, rbf and Polynomial kernel SVC are applied and accuracy scores are calculated on the test data. Also, a graph is plotted to show change of accuracy with change in "C" value.
Letter Recognition using non linear rbf kernel in SVM
PCA applied on images and Naive Bayes Classifier to classify them. Validation, cross validation and grid search with multi class SVM
RBF network implementation and demo in Java
Empowering Scientific Research with AI Assistance! Open Source Code for Data-Driven Dimensional Analysis.
Image Processing and classification using Machine Learning : Image Classification using Open CV and SVM machine learning model
SVR model which generates 3D-plots using Plotly. And It uses RBF kernel.
Classifying purchase events with introduction of dimensions to linearly separate the data points. The SVM algorithm uses Radial basis Function (RBF) Kernel.
We will apply soft-margin SVM to handwritten digits from the processed US Postal Service Zip Code data set.
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