Topic: linear-discriminant-analysis Goto Github
Some thing interesting about linear-discriminant-analysis
Some thing interesting about linear-discriminant-analysis
linear-discriminant-analysis,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.
User: aliamini93
linear-discriminant-analysis,Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
User: arnaldog12
linear-discriminant-analysis,Linear discriminant Analysis clustering Visualization for IRIS dataset
User: ayantika22
Home Page: https://github.com/Ayantika22/LDA-clustering-visualization-for-Iris-dataset
linear-discriminant-analysis,LDA(Linear discriminant Analysis) for Wine Dataset in machine learning
User: ayantika22
Home Page: https://github.com/Ayantika22/LDA-Linear-discriminant-Analysis-for-Wine-Dataset
linear-discriminant-analysis,Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning
User: ayantika22
Home Page: https://github.com/Ayantika22/Linear-discriminant-Analysis-LDA-for-Wine-Dataset
linear-discriminant-analysis,Unified multimodal classifier: a unified brain graph classification model trained on unpaired multimodal brain graphs, which can classify any brain graph of any size.
User: basiralab
linear-discriminant-analysis,The code for Fisher Discriminant Analysis (FDA) and Kernel Fisher Discriminant Analysis (Kernel FDA)
User: bghojogh
Home Page: https://arxiv.org/abs/1906.09436
linear-discriminant-analysis,implementation of some classification algorithms in c and c++
User: bhaveshgawri
linear-discriminant-analysis,Probabilistic graphical models home works (MVA - ENS Cachan)
User: chaoukia
linear-discriminant-analysis,Iris classification with Python Scikit-learn :blossom:
User: dehaoterryzhang
linear-discriminant-analysis,BrainVision EEG data classification using the MNE, Keras and the scikit-learn libraries.
User: fkupilik
linear-discriminant-analysis,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 discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
User: gionanide
linear-discriminant-analysis,To Detect Sepsis Disease using six Classifiers on clinical data
User: hcyendluri
linear-discriminant-analysis,
User: himansh18131018
linear-discriminant-analysis,A MATLAB toolbox for classifier: Version 1.0.7
User: hiroyuki-kasai
linear-discriminant-analysis,A Python implementation of RSLDA (paper "Robust Sparse Linear Discriminant Analysis").
User: hoangpham3003
Home Page: https://ieeexplore.ieee.org/document/8272002
linear-discriminant-analysis,Face recognition, tackling three different "old-school" Computer Vision techniques - as part of the Biometrics System Concepts course @ KU Leuven
User: ivonajdenkoska
linear-discriminant-analysis,This repository contains lecture notes and codes for the course "Computational Methods for Data Science"
User: jbramburger
linear-discriminant-analysis,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, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression
User: je-suis-tm
Home Page: https://je-suis-tm.github.io/machine-learning
linear-discriminant-analysis,Multi-sample Unified Discriminant ANalysis
User: jefworks
Home Page: http://jef.works/MUDAN/
linear-discriminant-analysis,This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.
User: jingweitoo
linear-discriminant-analysis,An implementation of [robust version] of Fisher Linear discriminant analysis
User: junlulocky
linear-discriminant-analysis,Application of Deep Learning and Feature Extraction in Software Defect Prediction
User: kaur-anupreet
linear-discriminant-analysis,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.
User: kbasu2016
linear-discriminant-analysis,CS385 homework. Logistic regression and LDA from scratch.
User: markdana
linear-discriminant-analysis,Approach at solving the problem of Face Recognition using dimensionality reduction algorithms like PCA and LDA
User: naman-ntc
linear-discriminant-analysis,Used to perform Ant Colony optimisation with Linear Discriminant Analysis for feature reduction in a dataset.
User: nikhil12321
linear-discriminant-analysis,Insurance claim fraud detection using machine learning algorithms.
User: nirab25
linear-discriminant-analysis,Liveness Tests For Facial Recognition
User: ohmgeek
linear-discriminant-analysis,Automated polysomnography for experimental animal research
User: paulbrodersen
linear-discriminant-analysis,In depth machine learning resources
User: puneet2000
linear-discriminant-analysis,A mobile application that diagnoses Parkinson’s disease patients using hand drawings
User: radhikaranasinghe
linear-discriminant-analysis,Final Year project based upon Network Intrusion Detection System
User: rahul-38-26-0111-0003
Home Page: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
linear-discriminant-analysis,Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (curse of dimensionality) and also reduce computational costs. Pertaining to our problem, we are given a [32 X 32] binary image as input and the goal is to apply LDA technique to transform the features into a lower dimensional space, which maximizes the ratio of the between-class variance to the within-class variance, thereby guaranteeing maximum class separability between two classes in our case with the minimal loss.
User: rajvi-patel-22
linear-discriminant-analysis,
User: sawadogosalif
linear-discriminant-analysis,Linear Discriminant Tree in jupyter notebook
User: shitian-ni
linear-discriminant-analysis,Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine
User: snatch59
linear-discriminant-analysis,Detection of Schizophrenia using Extreme Learning Machine
User: ssomnathssaha
linear-discriminant-analysis,We used LDA in this project to expand the capabilities of our Logistic Regression Classifier in both Python and R
User: stabgan
linear-discriminant-analysis,Machine learning library for classification tasks
Organization: starlangsoftware
linear-discriminant-analysis,Machine learning library for classification tasks
Organization: starlangsoftware
linear-discriminant-analysis,Machine learning library for classification tasks
Organization: starlangsoftware
linear-discriminant-analysis,简单易用的经典机器学习框架
User: sun1638650145
Home Page: https://classicml.readthedocs.io/
linear-discriminant-analysis,Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.
User: sushantdhumak
linear-discriminant-analysis,Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.
User: tatevkaren
linear-discriminant-analysis,Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning
User: timothygmitchell
linear-discriminant-analysis,Gaussian Discriminant Analysis introduction and Python implementation from scratch
User: tugrulhkarabulut
linear-discriminant-analysis,Face Recognition with SVM classifier using PCA, ICA, NMF, LDA reduced face vectors
User: tulsyanp
linear-discriminant-analysis,Information Retrieval in High Dimensional Data (class deliverables)
User: uzairakbar
linear-discriminant-analysis,implement the machine learning algorithms by python for studying
User: zhaoyichanghong
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