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Determining Provenance from Compositional Data (Elements in Current Archaeological Tools and Techniques) - Hardcover
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This volume highlights the classification methods in chemometrics, artificial intelligence, machine learning and knowledge discovery.
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Determining Provenance from Compositional Data (Elements in Current Archaeological Tools and Techniques)
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Hardcover. Zustand: new. Hardcover. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made. This Element reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data, thus obtaining better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups (common in biplots). To evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labeling, a semi-supervised algorithm is applied to obsidian samples. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. This Element reconsiders the use of statistical methods for provenance analysis of archaeological materials using a step-by-step procedure. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781009634168
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Determining Provenance from Compositional Data
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Hardback. Zustand: New. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made. This Element reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data, thus obtaining better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups (common in biplots). To evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labeling, a semi-supervised algorithm is applied to obsidian samples. Bestandsnummer des Verkäufers LU-9781009634168
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Determining Provenance from Compositional Data (Hardcover)
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Hardcover. Zustand: new. Hardcover. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made. This Element reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data, thus obtaining better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups (common in biplots). To evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labeling, a semi-supervised algorithm is applied to obsidian samples. Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. This Element reconsiders the use of statistical methods for provenance analysis of archaeological materials using a step-by-step procedure. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9781009634168
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Traditionally, classical multivariate statistical methods have been applied to relate cultural materials recovered at archaeological sites to their respective raw material sources. However, when reviewing published research, which usually claims to have reached a high degree of confidence in the assignment of materials, the authors have detected that those applying these methods can make serious errors that compromise the inferences made. This Element reconsiders the use of statistical methods to address the problem of provenance analysis of archaeological materials using a step-by-step procedure that allows the recognition of natural groups in the data, thus obtaining better quality classifications while avoiding the problems of total or partial overlaps in the chemical groups (common in biplots). To evaluate the methods proposed here, the challenge of group search in ceramic materials is addressed using algorithms derived from model-based clustering. For cases with partial data labeling, a semi-supervised algorithm is applied to obsidian samples. Bestandsnummer des Verkäufers 9781009634168
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