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linear-discriminant-analysis

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gionanide / Speech_Signal_Processing_and_Classification

Star 241

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can…

nlp classifier natural-language-processing feature-extraction nltk gaussian-mixture-models support-vector-machines mfcc principal-component-analysis speech-processing linear-discriminant-analysis isomap spectral-clustering long-short-term-memory kernel-pca spectral-embedding locally-linear-embedding linear-prediction-coefficients speech-utterance
  • Updated Mar 3, 2023
  • Python
machine-learning

je-suis-tm / machine-learning

Star 229

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

naive-bayes expectation-maximization support-vector-machine ridge-regression expectation-maximization-algorithm factor-analysis dbscan em-algorithm linear-discriminant-analysis stochastic-gradient-descent latent-semantic-analysis lasso-regression multinomial-naive-bayes singular-value-decomposition batch-gradient-descent sequential-minimal-optimization newton-method low-rank-approximation independent-component-analysis k-nearest-neighbors
  • Updated Dec 16, 2022
  • Jupyter Notebook

arnaldog12 / Machine_Learning

Star 219

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

python machine-learning neural-network naive-bayes linear-regression machine-learning-algorithms regression feature-selection logistic-regression kmeans adaboost decision-trees polynomial-regression knn principal-component-analysis redes-neurais-artificiais linear-discriminant-analysis multilinear-regression
  • Updated Apr 27, 2022
  • Jupyter Notebook

zhaoyichanghong / machine_learing_algo_python

Star 48

implement the machine learning algorithms by python for studying

machine-learning deep-learning neural-network linear-regression collaborative-filtering gaussian-mixture-models gbdt logistic-regression tf-idf kmeans adaboost support-vector-machines decision-tree principal-component-analysis linear-discriminant-analysis spectral-clustering isolation-forest k-nearest-neighbor rbf-network gaussian-discriminant-analysis
  • Updated Oct 24, 2019
  • Python

AliAmini93 / Fault-Detection-in-DC-microgrids

Star 35

Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

neural-network random-forest svm cnn feature-selection fft dwt data-augmentation mutual-information linear-discriminant-analysis pearson-correlation grid-search-hyperparameters xgboost-classifier anova-analysis digsilent
  • Updated Aug 25, 2024
  • Jupyter Notebook

kbasu2016 / Autism-Detection-in-Adults

Star 24

This is a binary classification problem related with Autistic Spectrum Disorder (ASD) screening in Adult individual. Given some attributes of a person, my model can predict whether the person would have a possibility to get ASD using different Supervised Learning Techniques and Multi-Layer Perceptron.

random-forest naive-bayes-classifier supervised-learning support-vector-machine data-wrangling decision-tree-classifier k-nearest-neighbours quadratic-discriminant-analysis linear-discriminant-analysis mlp-classifier data-cleansing logistic-regression-models
  • Updated May 15, 2018
  • Jupyter Notebook
Machine-Learning-Toolbox

JingweiToo / Machine-Learning-Toolbox

Star 22

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

data-science random-forest naive-bayes machine-learning-algorithms cross-validation classification gaussian-mixture-models support-vector-machine confusion-matrix decision-tree linear-discriminant-analysis holdout ensemble-classifier leave-one-out-cross-validation k-nearest-neighbor
  • Updated Jan 10, 2021
  • MATLAB

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