Open AccessPosted Content
Supervised and Unsupervised Classification for Pattern Recognition Purposes
Cătălina-Lucia Cocianu
- 13 Aug 2013
- Iss: 4, pp 5-13
TL;DR: The final section of the paper presents a new methodology for supervised learning based on PCA, provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern.
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Abstract: A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions
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Citations
Data Mining Algorithms for Knowledge Extraction
Stancu Ana-Maria Ramona,Cristescu Marian Pompiliu,Miglena Stoyanova +2 more
- 20 Sep 2019
TL;DR: From the studied algorithms, the clustering algorithms are emphasized, more precisely on the K-means algorithm, which was first studied using the Euclidean distance, then modified and studied the distance between the clusters.
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Hierarchical clustering algorithms and data security in financial management
Preda Bianca
- 01 Jan 2013
TL;DR: This paper presents the results of hierarchical clustering algorithms applied over an economic dataset that provides useful description of secured data for decision makers by comparison.
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Trevor Hastie,Robert Tibshirani,Jerome H. Friedman +2 more
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TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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Pattern Recognition with Fuzzy Objective Function Algorithms
James C. Bezdek
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TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
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Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
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Statistical methods for speech recognition
Frederick Jelinek
- 01 Jan 1997
TL;DR: The speech recognition problem hidden Markov models the acoustic model basic language modelling the Viterbi search hypothesis search on a tree and the fast match elements of information theory.
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Improved heterogeneous distance functions
D. Randall Wilson,Tony Martinez +1 more
TL;DR: This article proposed three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference metric (IVDM), and the Windowed Value Difference measure (WVDM) to handle applications with nominal attributes, continuous attributes and both.