Perpustakaan Fakultas Ilmu Komputer

Call Number	SEM - 362
Collection Type	Indeks Artikel prosiding/Sem
Title	Reduced space classification using kernel dimensionality reduction for question classification in public health wuestion- answering (hal 187 - 192)
Author	Hapnes Toba, Ito Wasito;
Publisher	Proceedings ICSIIT 2010: International conference on soft computing intelligent system and information technology 1-2 July 2010 Bali Indonesia
Subject	Kernel dimensionality reduction, reproducing kernel hilbert space, supervised machine learning, question classifaction question answering system
Location

Lokasi : Perpustakaan Fakultas Ilmu Komputer

Nomor Panggil	ID Koleksi	Status
SEM - 362		TERSEDIA

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one of the major priblems in question answering system is how to classify a question into a particular class that further will be used to find exact answers within a large collection of documents. kernel dimensionality reduction (KDR) is an alternative method that can be used for features reduction, and in the same time classify question type by using the most effective m-dimensional features in its vector space. in this expriment we used question-answer pairs data from public health domain and word (unigram) features construction. this research shows that KDR correct rate performance is better than SVMafter a head-to-head comparison from 100 observations.