Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Wed, 05 Dec 2018 22:36:00 GMT forecasting methods and applications 3rd pdf PDF | On Jan 1, 1984, S ~G Makridakis and others published Forecasting: Methods and Applications Tue, 04 Dec 2018 23:06:00 GMT (PDF) Forecasting: Methods and Applications ResearchGate Forecasting: methods and applications. This book was published in 1998, and for nearly 20 years I maintained an associated website at this address. Fri, 30 Nov 2018 14:35:00 GMT Forecasting: methods and applications | Rob J Hyndman Prod 2100-2110 Forecasting Methods 2 1. Framework of planning decisions Let us first remember where the inventory control decisions may take place. Fri, 07 Dec 2018 14:13:00 GMT Forecasting Methods UCLouvain 2002 Forecasting: Methods and Applications Makridakis, ... this 3rd edition very wisely includes some more advanced forecasting methods such as dynamic regression, ... Sat, 01 Dec 2018 22:41:00 GMT 2002 Forecasting: Methods and Applications HEPHAESTUS Methods and Applications Third Edition Spyros Makridakis European Institute of Business ... major forecasting methods 516 The use of different forecasting Tue, 04 Dec 2018 22:37:00 GMT Methods and Applications Max Planck Society MATH6011: Forecasting â€œAll models are wrong, ... S.C. and Hyndman, R.J. 1998, Forecasting: Methods and Applications 3rd Ed., New York: Wiley as text book. Wed, 21 Nov 2018 17:31:00 GMT MATH6011: Forecasting University of Southampton Save As PDF Ebook forecasting methods and applications ... FOUR LAMAS OF DOLPO AUTOBIOGRAPHIES OF FOUR TIBETAN LAMAS INTRODUCTION AND TRANSLATIONS VOL I 3RD [PDF] Tue, 04 Dec 2018 19:10:00 GMT forecasting methods and applications makridakis pdf ... forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online books in PDF, EPUB, Tuebl, and Mobi Format. Thu, 06 Dec 2018 07:26:00 GMT forecasting methods and applications 3rd ed | Download ... INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 : ... Some applications of forecasting ... Qualitative techniques in forecasting Time series methods Mon, 19 Nov 2018 11:49:00 GMT INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 ... 3 Hierarchical forecasting 9 3 Advanced methods 9. Forecasting: principles and practice 7 Assumptions â€¢ This is not an introduction to R. I assume you are broadly ... Thu, 06 Dec 2018 22:49:00 GMT Forecasting: Principles & Practice, Rob J Hyndman, 2014 forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online here in PDF or EPUB. Please click button to get ... Mon, 03 Dec 2018 08:27:00 GMT Forecasting Methods And Applications 3rd Ed | Download ... Forecasting methods can be classified as qualitative or quantitative. ... practical applications. 15-4 Chapter 15 Time Series Analysis and Forecasting Fri, 07 Dec 2018 12:33:00 GMT PDF Time Series Analysis and Forecasting Cengage FORECASTING METHODS AND APPLICATIONS 3RD EDITION PDF READ Forecasting Methods And Applications 3rd Edition pdf. Download Forecasting Methods And Applications 3rd ... Sun, 11 Nov 2018 17:14:00 GMT Free Forecasting Methods And Applications 3rd Edition PDF Forecasting Methods and Applications. 3rd ed. New York: John Wiley & Sons, 1998. Sat, 08 Dec 2018 09:40:00 GMT Forecasting Methods and Applications Book Harvard ... Preface In preparing the manuscript for the third edition of Forecasting: methods and applications, one of our primary goals has been to make the book as complete and ... Wed, 05 Dec 2018

Forecasting methods and applications.

In this paper, a new multi-criteria problem solving method—the Full Consistency Method (FUCOM)—is proposed. The model implies the definition of two groups of constraints that need to satisfy the optimal values of weight coefficients. The first group of constraints is the condition that the relations of the weight coefficients of criteria should be equal to the comparative priorities of the criteria. The second group of constraints is defined on the basis of the conditions of mathematical transitivity. After defining the constraints and solving the model, in addition to optimal weight values, a deviation from full consistency (DFC) is obtained. The degree of DFC is the deviation value of the obtained weight coefficients from the estimated comparative priorities of the criteria. In addition, DFC is also the reliability confirmation of the obtained weights of criteria. In order to illustrate the proposed model and evaluate its performance, FUCOM was tested on several numerical examples from the literature. The model validation was performed by comparing it with the other subjective models (the Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)), based on the pairwise comparisons of the criteria and the validation of the results by using DFC. The results show that FUCOM provides better results than the BWM and AHP methods, when the relation between consistency and the required number of the comparisons of the criteria are taken into consideration. The main advantages of FUCOM in relation to the existing multi-criteria decision-making (MCDM) methods are as follows: (1) a significantly smaller number of pairwise comparisons (only n − 1), (2) a consistent pairwise comparison of criteria, and (3) the calculation of the reliable values of criteria weight coefficients, which contribute to rational judgment.

https://www.mdpi.com/2073-8994/10/9/393/pdf

A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM)

Desirability function approach: A review and performance evaluation in adverse conditions

This paper presents a location selection problem for a military airport using multiple criteria decision making methods. A real-world decision problem is presented and the decision criteria to evaluate alternative locations are specified. The objective is to identify the best location among candidate locations. Nine main criteria and thirty-three sub-criteria are identified by taking into account not only requirements for a military airport such as climate, geography, infrastructure, security, and transportation but also its environmental and social effects. The criteria weights are determined using AHP. Ranking and selection processes of four alternatives are carried out using PROMETHEE and VIKOR methods. Furthermore, the results of PROMETHEE and VIKOR methods are compared with the results of COPRAS, MAIRCA and MABAC methods. All methods suggest the same alternative as the best and produce the same results on the rankings of the location alternatives. One-way sensitivity analysis is carried out on the main criteria weights for all methods. Statistically significant correlations are observed between the rankings of the methods. Therefore, it is concluded that PROMETHEE, VIKOR, COPRAS, MAIRCA and MABAC methods can be successfully used for location selection problems and in general, for other types of multi-criteria decision problems with finite number of alternatives.

A military airport location selection by AHP integrated PROMETHEE and VIKOR methods

Siting of a central healthcare waste incinerator using GIS-based Multi-Criteria Decision Analysis

A framework to determine the locations of temporary storage sites is proposed.A sustainable solution methodology for disaster waste management is provided.The framework not only considers the cost but also the side effects on human beings.Utilization of debris is a gap in research and this study aims to fill this gap.This is the only study using NSGA-II to solve debris utilization problem. Disasters can lead to a high risk of casualties and structural damages. Destructive disasters, such as earthquakes, may cause a great amount of disaster waste to be controlled. Reusing and recycling materials in the debris can decrease the need for re-construction resources. These reusable or recyclable materials can be processed in temporary storage sites like the ones suggested by the United Nations and the United States Federal Emergency Management Agency guidelines on disaster waste management. The objective of this paper is to build a framework for determining the locations of temporary storage facilities, and includes planning for the collection and transportation of disaster waste in order to manage it in an environmentally sustainable way. In this study, a multi-objective optimization model is developed and solved with an evolutionary elitist multi-objective optimization algorithm (NSGA-II). As a city prone to high earthquake damage, Istanbul has been selected for the illustration of the proposed framework. The objectives of the model are cost minimization and minimization of risk from hazardous waste exposure. The study integrates disaster loss estimation methods with post-disaster waste management.

An evolutionary multi-objective optimization approach to disaster waste management

Measuring satisfaction of patients and understanding their perceptions are important for the hospital management to respond to customers' growing demands for acceptable quality of healthcare services. The main purpose of this paper is to present a methodology that can be used in the healthcare service quality evaluation. IVIF-PROMETHEE method is applied for service quality evaluation based on patients' views in a real-life case study of a public hospital in Istanbul and the results are compared with the results from IVIF-TOPSIS method. Interval Valued Intuitionistic Fuzzy (IVIF) set is considered an appropriate tool because of the complexity and vagueness of the evaluation of healthcare services. The evaluation of the quality level of the healthcare service provides detailed information about the effects of considered criteria as well as creates opportunities in implementing effective procedures to overcome service quality problems via analysing the patients' views. The proposed methodology can be a useful tool for hospital management.

Bahar Sennaroglu

Papers

A military airport location selection by AHP integrated PROMETHEE and VIKOR methods

Siting of a central healthcare waste incinerator using GIS-based Multi-Criteria Decision Analysis

An evolutionary multi-objective optimization approach to disaster waste management

Hospital service quality evaluation with IVIF-PROMETHEE and a case study

Optimization of chemical admixture for concrete on mortar performance tests using mixture experiments