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Machine learning and the economy
Published in Siddhartha Mitra, Robotization and Economic Development, 2023
In the case of unsupervised learning, the computer is given data so that on the basis of algorithms it can detect patterns in the data for categorization on the basis of similarities: for example, flat addresses can be bunched according to the rent category these belong to or other similarities in the data pertaining to flats; or stories posted on a website can be bunched by topic. Again, notice that such learning enables a lot of time saving by humans; in regard to the example elaborated above, the consumer interested in renting a flat from a given rent category in a given geographical area in a city would save a lot of time on account of unsupervised machine learning because the computer system itself would be able to present the flat addresses available for rent, from a narrow geographical area, which belong to the rent category in which the consumer is interested. The time-saving itself would be transformed into economic benefits as the consumers would be able to use the saved time for productive economic activity. A similar benefit would be generated in the example of “stories bunched by topic” as the consumer would be able to direct her attention to the stories pertaining to the topic that interests her without incurring any time costs in identifying and compiling stories.
Document Categorization
Published in John Atkinson-Abutridy, Text Analytics, 2022
Document categorization is the text mining task that consists of assigning a predefined class label to a document from models generated from training corpuses. The categorization task is present in many business, industrial, and scientific applications such as sentiment analysis, email classification, document filtering, client profile categorization, relationship identification in scientific literature, etc. There are several categorization methods based on Bayesian models, neural network models, maximum entropy methods, etc. Some of those make many assumptions about the distribution of the data (i.e., Bayesian approaches), while others minimize compromise on the data (i.e., maximum entropy). The standard evaluation considers Accuracy, Precision, and Recall measures, which allow evaluating the performance of classifiers but compare them in the analysis of large corpuses.
Functional Architecture for Knowledge Categorization
Published in Denise Bedford, Knowledge Architectures, 2020
Categorizing is a distinct process from the development of a categorization scheme (Borko, 1964; Boros et al., 1998; Ciesiak & Chowla, 2009; Hammer et al., 2004; Hanson & Brennan, 1990; Lee & Brennan, 2009; Lee et al., 2002; Sayers, 1918). These two processes are often conflated into a single action. When conflated, the result is both a suboptimized schema and suboptimized categorization of entities into groups. Categorization is the act of distributing or assigning objects into classes or groups based on common attributes or relationships. Categorization is a decision-making process that involves making choices. Decisions are frequently made in the context of an existing categorization scheme by a human or machine categorizer and for a given object (Figure 13.3). In theory, this choice seems like a straightforward decision process. The categorizer knows the categorization scheme, considers the object and what they know about the possible categories, and chooses the categories that are the best for the object.
Toward an information theoretic ontology of risk, resilience and sustainability and a blueprint for education - part II
Published in Sustainable and Resilient Infrastructure, 2022
Linda Nielsen, Michael Havbro Faber
In a position known as essentialism, there are two kinds of properties: essential and accidental. The former capture those things without which a thing would not be that kind of thing. They are, in other words, the necessary and sufficient conditions for a thing to be that kind of thing. Natural kinds then are the objective categories of the entities existing in the world. Being purely objective, they are independent of perception and linked in a system of logical relations. Truth (meaning) in correspondence theory is determined either based on Fregean sense-reference functions (Frege, 1892) or on Kripke-Putnam’s causal theory of pointing and naming (Kripke (1972), Putnam (1975)), both of which depend on the assumption of correspondence between symbols in a natural or formal language and a physical world that is independent of any perception. Categorization relies on a set-theoretic methodology whereby an item is classified as a member of a set (a category) based on the inherent shared properties of members of the set and in accordance with binary logic. Everything that exists is either in the particular set or outside it.
Where Did Knowledge Management Go?: A Comprehensive Survey
Published in Cybernetics and Systems, 2021
Rodrigo Oliveira de Castro, Cesar Sanin, Edward Szczerbicki, Andrew Levula
Classification scheme is a term used to describe a process of categorizing an object through content analysis and grouped them by similarity (Fteimi and Lehner 2018). It is a method that utilize synonyms such as framework, taxonomy, or typology (Nickerson, Varshney, and Muntermann 2013; Gregor 2006; Bailey 1994) and it refers to the outcome of a classification approach for multiple entities. The categorization process describes the function of structuring different things into (n) categories or groups, which can be sub-categorized (Bailey 1994). A classification scheme, helps to clarify and evaluate the complexity of a research domain, simplifies and crates of a common vocabulary and improves the efficiency of database searches through a unique terminology (Barki, Rivard, and Talbot 1988).
Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence
Published in The Imaging Science Journal, 2023
M. Sumathi, P. Chitra, S. Sheela, C. Ishwarya
One of the most popular supervised learning algorithms is SVM which is used to tackle both classification and regression issues. However, it is primarily utilised in machine learning to address categorization problems. In order to quickly classify additional data points in the future, the SVM approach aims to define the best line or result border that can categorize n-dimensional space. This top conclusion boundary is known as a hyper plane. SVM selects the extreme vectors and points that will aid in the formation of the hyper plane. The method is known as a support vector machine because these unique situations are defined by support vectors. The Architecture of SVM is shown in Figure 6.