Information Retrieval in High Dimensional Data (class deliverables)
svm embeddings neural-networks logistic-regression principal-component-analysis convex-optimization linear-discriminant-analysis kernel-trick
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kernel-trick
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PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)
deep-learning generative-model kernels optimal-transport kernel-trick image-to-image-translation neural-optimal-transport weak-optimal-transport
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Comparison of principal components analysis with diffusion maps on toy data sets and a molecular simulation trajectory
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- Fortran
Multi-Shot Approximation of Discounted Cost MDPs
reinforcement-learning linear-programming reinforcement-learning-algorithms inventory-management value-function kernel-trick approximate-dynamic-programming random-features
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- Python
To deal with non-linearly separable we use SVM's Kernel Trick which maps data to higher dimension!
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University of Washington: CSE 446 (WIN '17) Machine Learning
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A perceptron optimized with Numba that makes use of dual coordinates and kernel functions instead of classical dot product
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- Python
This repository contains my projects in the context of the "Computational Intelligence" course (held at FUM).
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This repository contains the projects and homework from CSC 591 Graph Data Mining
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packer identification tool using SVM
windows pefile machine-learning svm ml similarity similarity-measures svm-classifier kernel-trick sequence-analysis
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- Python
📊 Predicting stock price direction using Support Vector Machine (SVM), with a focus on both theoretical foundations and practical implementation. Includes evaluation via confusion matrix, classification report, and heatmap visualization.
machine-learning ai kernel svm ml prediction artificial-intelligence support-vector-machines svm-model svm-classifier kernel-trick stock-prediction-models
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My first steps to becoming AI engineer :)
python java machine-learning ai matlab svm coursera cnn dimensional-analysis pca-analysis pca classification metric-learning k-means knn fuzzy-logic andrew-ng kernel-trick k-medoids
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In this project I developed a program that generates nonlinearly separable data. This data is mapped into a higher dimension using a kernel trick where the data becomes linearly separable
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- Python
Exploring meachine learning techniques and algorithms. Including clustering algorithms, perceptron and, more.
python machine-learning clustering scikit-learn perceptron k-means dbscan kernel-trick agglomerative kernel-approximation rand-index kernelized-perceptron
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- HTML
Implemented a multi-class SVM that supports multi-core parallel computing
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- Python
A multiclass Support Vector Machine that supports kernel tricks has been implemented by solving the SVM quadratic programming problem.
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- Python
Machine Learning Algorithms
tic-tac-toe regression ensemble-learning decision-trees ridge-regression kernel-trick lasso-regression candidate-elimination graph-neural-networks find-s smote-sampling bagging-ensemble boosting-ensemble
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Gaussian Processes for Machine Learning
python machine-learning kernel regression bayesian-inference gaussian-processes kernel-trick gaussian-process-regression
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Understanding of Support Vector Machines (SVMs) and their implementations. Covers linear and non-linear SVM, also explains how to use the "kernel trick" to handle non-linearly separable data.
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