Lazy Learning: A Logical Method for Supervised Learning

2002 ◽  
pp. 97-136 ◽  
Author(s):  
G. Bontempi ◽  
M. Birattari ◽  
H. Bersini
Keyword(s):  
Supervised Learning ◽  
Lazy Learning ◽  
Logical Method

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

An IoT Device Identification Method based on Semi-supervised Learning

2020 ◽  
Author(s):  
Linna Fan ◽  
Shize Zhang ◽  
Yichao Wu ◽  
Zhiliang Wang ◽  
Chenxin Duan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Device Identification ◽  
Identification Method

Particularities of the independent auditor's report on reliability of accounting (financial) statements for 2019: COVID-19 as qualitatively essential information (Part 2)

2020 ◽  
Vol 23 (7) ◽  
pp. 777-799
Author(s):  
O.I. Shvyreva ◽  
Z.I. Kruglyak ◽  
A.V. Petukh
Keyword(s):  
Financial Reporting ◽  
Financial Statements ◽  
Economic Situation ◽  
Risk Disclosure ◽  
Essential Information ◽  
Logical Method ◽  
Annual Reporting ◽  
The Face ◽  
Key Aspects ◽  
The Impact

Subject. This article discusses the issues related to the practice of financial reporting in the face of uncertainties caused by the coronavirus contagion, as well as the specifics of the audit strategy and formation of an audit opinion on this reporting. Objectives. The article aims to identify the quality characteristics of financial reporting prepared in the context of the COVID-19 pandemic and justify the key aspects of assurance engagement completion in an extremely uncertain epidemiological and economic situation. Methods. For the study, we used an abstract-logical method, content analysis techniques, systematization, and classification. Results. Analyzing the impact of the extremely uncertain epidemiological and economic situation on financial statements, the article clarifies aspects of disclosure of events after the reporting date and threats to business continuity in the annual reporting of economic entities. The article identifies possible alternative procedures and algorithms to obtain proper evidence when it is insufficient in the face of the inability to meet certain audit standards requirements in a remote audit environment. The article defines the impact of COVID-19 risk disclosure on the structure of the audit report and opinion. Relevance. The results of the study can be used in the practical activities of economic entities that prepare financial statements in the face of significant uncertainty, as well as auditors and audit organizations.

Pembobotan Berdasarkan Tingkat Kesamaan Semantik pada Metode Fuzzy Semi-Supervised Co-Clustering untuk Pengelompokkan Dokumen Teks

2014 ◽  
Vol 6 (2) ◽  
pp. 46-51
Author(s):  
Galang Amanda Dwi P. ◽  
Gregorius Edwadr ◽  
Agus Zainal Arifin
Keyword(s):  
Supervised Learning ◽  
Semantic Similarity ◽  
The Other ◽  
Classification Result ◽  
Document Similarity ◽  
The Matrix ◽  
Index Terms ◽  
Membership Value ◽  
Degree Of Similarity

Nowadays, a large number of information can not be reached by the reader because of the misclassification of text-based documents. The misclassified data can also make the readers obtain the wrong information. The method which is proposed by this paper is aiming to classify the documents into the correct group.  Each document will have a membership value in several different classes. The method will be used to find the degree of similarity between the two documents is the semantic similarity. In fact, there is no document that doesn’t have a relationship with the other but their relationship might be close to 0. This method calculates the similarity between two documents by taking into account the level of similarity of words and their synonyms. After all inter-document similarity values obtained, a matrix will be created. The matrix is then used as a semi-supervised factor. The output of this method is the value of the membership of each document, which must be one of the greatest membership value for each document which indicates where the documents are grouped. Classification result computed by the method shows a good value which is 90 %. Index Terms - Fuzzy co-clustering, Heuristic, Semantica Similiarity, Semi-supervised learning.

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