Support vector machines with example dependent costs

Ulf Brefeld; Peter Geibel; Fritz Wysotzki

doi:10.1007/978-3-540-39857-8_5

Support vector machines with example dependent costs

Publikation: Beiträge in Zeitschriften › Konferenzaufsätze in Fachzeitschriften › Forschung › begutachtet

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Support vector machines with example dependent costs. / Brefeld, Ulf; Geibel, Peter; Wysotzki, Fritz.
in: Lecture Notes in Computer Science, Jahrgang 2837, 01.01.2003, S. 23-34.

Publikation: Beiträge in Zeitschriften › Konferenzaufsätze in Fachzeitschriften › Forschung › begutachtet

Bibtex

@article{d62c64bc14e24a8ba0fe188cf1dd7589,

title = "Support vector machines with example dependent costs",

abstract = "Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relation to the Bayes rule. We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.",

keywords = "Informatics, support vector machine, Cost matrix, Soft margin, Support Vector Machines (SVM), Dependent Cost, Business informatics",

author = "Ulf Brefeld and Peter Geibel and Fritz Wysotzki",

year = "2003",

month = jan,

day = "1",

doi = "10.1007/978-3-540-39857-8_5",

language = "English",

volume = "2837",

pages = "23--34",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland",

}

RIS

TY - JOUR

T1 - Support vector machines with example dependent costs

AU - Brefeld, Ulf

AU - Geibel, Peter

AU - Wysotzki, Fritz

PY - 2003/1/1

Y1 - 2003/1/1

N2 - Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relation to the Bayes rule. We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.

AB - Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relation to the Bayes rule. We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.

KW - Informatics

KW - support vector machine

KW - Cost matrix

KW - Soft margin

KW - Support Vector Machines (SVM)

KW - Dependent Cost

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=9444295412&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-39857-8_5

DO - 10.1007/978-3-540-39857-8_5

M3 - Conference article in journal

AN - SCOPUS:9444295412

VL - 2837

SP - 23

EP - 34

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -