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smogn

Synthetic Minority Over-Sampling Technique for Regression

smogn-1

Python Implementatin of SMOGN

smogn-lidta17

SMOGN: a Pre-processing Approach for Imbalanced Regression - LIDTA2017

smote-oversample-rare-events

Youtube companion (https://www.youtube.com/watch?v=1Mt7EuVJf1A&feature=youtu.be) - Brief introduction to the SMOTE R package to super-sample/ over-sample imbalanced data sets. SMOTE will use bootstrapping and k nearest neighbor to synthetically create additional observations. SMOTE white paper: https://www.jair.org/media/953/live-953-2037-jair.pdf

sna

Social Network Analysis for Migration in Colombia.

sna2019open

sna_hackaton

snacks

Snack size awesome list for Social Network Analysis resources

snap

Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.

snap-python

SNAP Python code, SWIG related files

snli-entailment

attention model for entailment on SNLI corpus implemented in Tensorflow and Keras

snns

Tutorials and implementations for "Self-normalizing networks"

snorkel

A lightweight platform for developing information extraction systems using data programming

soc

Search Online Collections

soccer-league-web-scraping

Scraping the data of English Soccer Leagues from a statical webpage.

social-media-insight-using-naive-bayes

we will look at disambiguating the use of the term Python on Twitter's stream. When people talk about Python, they could be talking about the following things: The programming language Python Monty Python, the classic comedy group The snake Python A make of shoe called Python There can be many other things called Python. The aim of our experiment is to take a tweet mentioning Python and determine whether it is talking about the programming language, based only on the content of the tweet.

social-network-analysis-using-r

Analyze the pattern for refugee migration in world after 1980

social-recommendation

Automatically exported from code.google.com/p/social-recommendation

social_influencer

Ranking Influencers on Social Collaboration Platforms

social_media_based_hr_analytics

Human Resources analytics on twitter data

social_ntwks

Repository with teaching materials for "Structural Analysis and Visualization of Networks" class

socializedwordembeddings

socialmediarumorstancedetection

This repository will contain code for our upcoming paper "A Little Birdie Told Me ... " - Inductive Biases for Rumour Stance Detection on Social Media

societyrecommendersystem

Based on the extracted data of the primary indicators(GDP, HDI, Literacy Rate, Life Expectancy, Exports, Income….) of Newly Industrialized 15 countries like Sri Lanka, Brazil, Argentina [source : worldbank.org], a Content Based Recommendation System is trained by using Artificial Neural Networks and based on the recommender, sectors which need attention are recommended.

socviz

sofia-ml

Automatically exported from code.google.com/p/sofia-ml

sofia-ml-mod

sofia-kmeans with sparse RBF cluster mapping

soft-dtw

Python implementation of soft-DTW.

softimpute

:exclamation: This is a read-only mirror of the CRAN R package repository. softImpute — Matrix Completion via Iterative Soft-Thresholded SVD

software-reliability-prediction-using-ensemble-learning

Software reliability is an indispensable part of software quality and is one among the most inevitable aspect for evaluating quality of software product. Software Industry endures various challenges in developing highly reliable software. We use the ensemble methods and machine learning techniques for software reliability predictions and evaluate them based on selected performance criteria. Ensemble learning, a machine learning paradigm where multiple base learners are trained to solve the problem. Here a set of hypotheses are constructed and combined to give improved results. Software reliability modelling based on test data is done to estimate whether the current reliability level meets the requirements for the product software reliability modelling also provides possibility to predict the reliability of the modules in a software. In this proposed work we have used ensemble methods on various machine learning approaches and study the ensemble performance using bagging, boosting and stacking. Using ensembling, a module in a software is classified whether to have faults or not based on certain attributes of the software module and predictions of the next failure in the software given the Mean Time Between Failure.