Semantic Scholar Open Access 2004 1483 sitasi

Support vector machine learning for interdependent and structured output spaces

Ioannis Tsochantaridis Thomas Hofmann T. Joachims Y. Altun

Lihat Sumber DOI

Abstrak

Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits the sparseness and structural decomposition of the problem. We demonstrate the versatility and effectiveness of our method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.

Topik & Kata Kunci

Computer Science

Penulis (4)

Ioannis Tsochantaridis

Thomas Hofmann

T. Joachims

Y. Altun

Format Sitasi

APA MLA BibTeX

Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y. (2004). Support vector machine learning for interdependent and structured output spaces. https://doi.org/10.1145/1015330.1015341

Akses Cepat

Lihat di Sumber doi.org/10.1145/1015330.1015341

Informasi Jurnal

Tahun Terbit: 2004
Bahasa: en
Total Sitasi: 1483×
Sumber Database: Semantic Scholar
DOI: 10.1145/1015330.1015341
Akses: Open Access ✓