KELP is a Java framework that enables fast and easy implementation of kernel functions over discrete data, such as strings, trees or graphs and their combination with standard vectorial kernels. Additionally, it provides several kernel- based algorithms, e.g., online and batch kernel machines for classification, regression and clustering, and a Java environment for easy implementation of new algorithms. KELP is a versatile toolkit, very appealing both to experts and practitioners of machine learning and Java language programming, who can find extensive documentation, tutorials and examples of increasing complexity on the accompanying website. Interestingly, KELP can be also used without any knowledge of Java programming through command line tools and JSON/XML interfaces enabling the declaration and instantiation of articulated learning models using simple templates. Finally, the extensive use of modularity and interfaces in KELP enables developers to easily extend it with their own kernels and algorithms.

KELP: a Kernel-based Learning Platform / Filice, Simone; Castellucci, Giuseppe; Da San Martino, Giovanni; Moschitti, Alessandro; Croce, Danilo; Basili, Roberto. - In: JOURNAL OF MACHINE LEARNING RESEARCH. - ISSN 1532-4435. - 18:(2018), pp. 191.1-191.5.

KELP: a Kernel-based Learning Platform

Alessandro Moschitti;
2018-01-01

Abstract

KELP is a Java framework that enables fast and easy implementation of kernel functions over discrete data, such as strings, trees or graphs and their combination with standard vectorial kernels. Additionally, it provides several kernel- based algorithms, e.g., online and batch kernel machines for classification, regression and clustering, and a Java environment for easy implementation of new algorithms. KELP is a versatile toolkit, very appealing both to experts and practitioners of machine learning and Java language programming, who can find extensive documentation, tutorials and examples of increasing complexity on the accompanying website. Interestingly, KELP can be also used without any knowledge of Java programming through command line tools and JSON/XML interfaces enabling the declaration and instantiation of articulated learning models using simple templates. Finally, the extensive use of modularity and interfaces in KELP enables developers to easily extend it with their own kernels and algorithms.
2018
Filice, Simone; Castellucci, Giuseppe; Da San Martino, Giovanni; Moschitti, Alessandro; Croce, Danilo; Basili, Roberto
KELP: a Kernel-based Learning Platform / Filice, Simone; Castellucci, Giuseppe; Da San Martino, Giovanni; Moschitti, Alessandro; Croce, Danilo; Basili, Roberto. - In: JOURNAL OF MACHINE LEARNING RESEARCH. - ISSN 1532-4435. - 18:(2018), pp. 191.1-191.5.
File in questo prodotto:
File Dimensione Formato  
16-087.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 253.2 kB
Formato Adobe PDF
253.2 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/251974
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 18
  • ???jsp.display-item.citation.isi??? 7
  • OpenAlex ND
social impact