Search Results for author: Chaehan So

Found 6 papers, 0 papers with code

Understanding the Prediction Mechanism of Sentiments by XAI Visualization

no code implementations3 Mar 2020 Chaehan So

The present work aimed to gain an understanding of a machine learning model's prediction mechanism by visualizing the effect of sentiments extracted from online hotel reviews with explainable AI (XAI) methodology.

BIG-bench Machine Learning Feature Importance

Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice

no code implementations29 Feb 2020 Chaehan So

This paper analyzes how candidate choice prediction improves by different psychological predictors.

Human-in-the-Loop Design Cycles -- A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans

no code implementations29 Feb 2020 Chaehan So

Hence, this work proposes a new process framework, Human-in-the-learning-loop (HILL) Design Cycles - a design process that integrates the structural elements of agile and design thinking process, and controls the training of a machine learning model by the human in the loop.

BIG-bench Machine Learning

Are You an Introvert or Extrovert? Accurate Classification With Only Ten Predictors

no code implementations29 Feb 2020 Chaehan So

This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors.

General Classification

What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI

no code implementations29 Feb 2020 Chaehan So

The current work analyzed these online reviews by sentiment analysis and used the extracted sentiments as features to predict the product ratings by several machine learning algorithms.

BIG-bench Machine Learning Feature Importance +1

A Pragmatic AI Approach to Creating Artistic Visual Variations by Neural Style Transfer

no code implementations28 May 2018 Chaehan So

To this aim, the present work explores a well-documented neural style transfer algorithm (Johnson 2016) in four experiments on four relevant visual parameters: number of iterations, learning rate, total variation, content vs. style weight.

Style Transfer

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