How to Assign Weights to Values?

2020 ◽  
Vol 12 (2) ◽  
pp. 111-118
Author(s):  
Silviya Serafimova ◽  
Keyword(s):  
Machine Learning ◽  
Human Behavior ◽  
Positive Impact ◽  
Machine Learning Techniques ◽  
Learning Techniques

In this paper, one of my primary objectives is to analyze why adopting particular machine-learning techniques and using a moral AI as an adviser is an insufficient condition for eradicating racist human attitudes. By outlining some difficulties in justifying what artificial “explicit ethical agents” in Moor’s sense should look like, I explore why, even if the development of machine-learning techniques can be accepted in epistemic terms, it does not follow that the techniques in question will have a positive impact in changing immoral human behavior.

The potential of machine learning techniques for expert systems

1989 ◽  
Vol 3 (3) ◽  
pp. 175-193 ◽  
Author(s):  
Yoram Reich ◽  
Steven J. Fenves
Keyword(s):  
Machine Learning ◽  
Expert Systems ◽  
Human Behavior ◽  
Structural Design ◽  
Partial Solution ◽  
Machine Learning Techniques ◽  
Research Directions ◽  
Learning Techniques

Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious.Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.

scholarly journals Human Behavior Prediction based on Opinions using Machine Learning Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 3117-3120
Keyword(s):  
Machine Learning ◽  
Human Behavior ◽  
Machine Learning Techniques ◽  
Algorithm Analysis ◽  
Learning Approaches ◽  
Behavior Prediction ◽  
Web Crawler ◽  
Learning Techniques ◽  
Pos Tagger ◽  
The Web

Prediction is the way of identifying the behavior of a person towards online shopping by analyzing the reviews publicly available on the web. In the present study, machine learning approaches are used to extract reviews from the web and segregate and classify them in to five categories, namely, strongly positive, positive, neutral, negative, and strongly negative, for the prediction of human behavior. Several pre-processing methods (including stop-word removal) are applied and web crawler is used to gather the data. This is followed by the application of Stanford POS tagger for tagging the reviews, which is done after stemming by using the porter stemmer algorithm. Analysis of a person’s behavior is performed and experimental results are compared with machine learning approaches.

Prospect for Knowledge in Survey Data

2017 ◽  
Vol 36 (5) ◽  
pp. 575-590
Author(s):  
Patrick Weber ◽  
Nicolas Weber ◽  
Michael Goesele ◽  
Rüdiger Kabst
Keyword(s):  
Machine Learning ◽  
Human Behavior ◽  
Voter Turnout ◽  
European Social Survey ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Unknown Variable ◽  
Learning Techniques ◽  
Behavioral Aspects ◽  
Human Decision

Policy making depends on good knowledge of the corresponding target audience. To maximize the designated outcome, it is essential to understand the underlying coherences. Machine learning techniques are capable of analyzing data containing behavioral aspects, evaluations, attitudes, and social values. We show how existing machine learning techniques can be used to identify behavioral aspects of human decision-making and to predict human behavior. These techniques allow to extract high resolution decision functions that enable to draw conclusions on human behavior. Our focus is on voter turnout, for which we use data acquired by the European Social Survey on the German national vote. We show how to train an artificial expert and how to extract the behavioral aspects to build optimized policies. Our method achieves an increase in adjusted R2 of 102% compared to a classic logistic regression prediction. We further evaluate the performance of our method compared to other machine learning techniques such as support vector machines and random forests. The results show that it is possible to better understand unknown variable relationships.

scholarly journals Behavior Prophecy of Stock Trader using Machine Learning Techniques

2019 ◽  
Vol 8 (11) ◽  
pp. 3620-3624
Keyword(s):  
Machine Learning ◽  
Human Behavior ◽  
User Behavior ◽  
Machine Learning Techniques ◽  
Web Based ◽  
Learning Techniques ◽  
Prediction Technique ◽  
Irregular Behavior ◽  
The Web ◽  
Unique Application

The web utilization by users is expanding very rapidly. Users are getting to data and administrations effectively through different media like social correspondence, sight and sound substance, web based trading, banking administrations and so forth. It winds up provoking undertaking to precisely recognize and separate typical and suspicious human behavior conduct. Every unique application need to predict user behavior to forecast and upgrade their administration quality. This work gives the examination of stock trader conduct recognition and expectation. Many Machine Learning (ML) methods and recognizable proof strategies are looked at and examined for stock trader behavior analysis. Their parameters are considered and enhancements are recommended. The proposed procedure portrays stock trader conduct discovery framework. The vital segment examination is the classification and prediction technique used to recognize and understand the typical and irregular behavior of the stock trader.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Sign in / Sign up

Export Citation Format

Share Document