Building intelligent alarm systems by combining mathematical models and inductive machine learning techniques

1996 ◽  
Vol 41 (2) ◽  
pp. 107-124 ◽  
Author(s):  
Bert Müller ◽  
A. Hasman ◽  
J.A. Blom
Keyword(s):  
Machine Learning ◽  
Mathematical Models ◽  
Machine Learning Techniques ◽  
Alarm Systems ◽  
Learning Techniques ◽  
Inductive Machine Learning

scholarly journals EXPLORING THE STACKING STATE-SPACE

2002 ◽  
Vol 11 (02) ◽  
pp. 267-282 ◽  
Author(s):  
AGAPITO LEDEZMA ◽  
RICARDO ALER ◽  
DANIEL BORRAJO
Keyword(s):  
Machine Learning ◽  
State Space ◽  
Linear Response ◽  
Search Space ◽  
Learning Systems ◽  
Hill Climbing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Inductive Machine Learning ◽  
Simple Search

Nowadays, there is no doubt that machine learning techniques can be successfully applied to data mining tasks. Currently, the combination of several classifiers is one of the most active fields within inductive machine learning. Examples of such techniques are boosting, bagging and stacking. From these three techniques, stacking is perhaps the less used one. One of the main reasons for this relates to the difficulty to define and parameterize its components: selecting which combination of base classifiers to use, and which classifier to use as the meta-classifier. One could use for that purpose simple search methods (e.g. hill climbing), or more complex ones (e.g. genetic algorithms). But before search is attempted, it is important to know the properties of the search space itself. In this paper we study exhaustively the space of Stacking systems that can be built by using four base learning systems: C4.5, IB1, Naive Bayes, and PART. We have also used the Multiple Linear Response (MLR) as meta-classifier. The properties of this state-space obtained in this paper will be useful for designing new Stacking-based algorithms and tools.

scholarly journals WHAT PERSONALISATION CAN DO FOR YOU! OR, HOW TO DO RACIAL DISCRIMINATION WITHOUT RACE

2021 ◽  
Author(s):  
Scott Wark ◽  
Thao Phan
Keyword(s):  
United States ◽  
Machine Learning ◽  
Asian American ◽  
Language Use ◽  
The United States ◽  
Machine Learning Techniques ◽  
Life And Death ◽  
Demographic Group ◽  
Learning Techniques ◽  
Inductive Machine Learning

Between 2016 and 2020, Facebook allowed advertisers in the United States to target their advertisements using three broad “ethnic affinity” categories: “African American,” “U.S.-Hispanic,” and “Asian American.” This paper uses the life and death of these “ethnic affinity” categories to argue that they exemplify a novel mode of racialisation made possible by machine learning techniques. These categories worked by analysing users’ preferences and behaviour: they were supposed to capture an “affinity” for a broad demographic group, rather than registering membership of that group. That is, they were supposed to allow advertisers to “personalise” content for users depending on behaviourally determined affinities. We argue that, in effect, Facebook’s ethnic affinity categories were supposed to operationalise a “post-racial” mode of categorising users. But the paradox of personalisation is that in order to apprehend users as individuals, platforms must first assemble them into groups based on their likenesses with other individuals. This article uses an analysis of these categories to argue that even in the absence of data on a user’s race—even after the demise of the categories themselves—users can still be subject to techniques of inclusion or exclusion for discriminatory ends. The inductive machine learning techniques that platforms like Facebook employ to classify users generate “proxies,” like racialised preferences or language use, as racialising substitutes. This article concludes by arguing that Facebook’s ethnic affinity categories in fact typify novel modes of racialisation today.

Design synthesis knowledge and inductive machine learning

2001 ◽  
Vol 15 (3) ◽  
pp. 233-249 ◽  
Author(s):  
S. POTTER ◽  
M.J. DARLINGTON ◽  
S.J. CULLEY ◽  
P.K. CHAWDHRY
Keyword(s):  
Machine Learning ◽  
Conceptual Design ◽  
Design Process ◽  
Learning Algorithm ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Design Synthesis ◽  
Conceptual Design Phase ◽  
Learning Techniques ◽  
Inductive Machine Learning

A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.

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