Huma Lodhi · VLDB 2026 Research

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 8 · 6 first-authorApplied, interdisciplinary, general and emerging computing · 4 · 3 first-authorDatabases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 2 papers		Information extraction and text analysis · 70% Kernel, tree and ensemble methods · 30%
Databases, data mining, and information retrieval 2 papers		Information retrieval · 54% Machine learning and data management · 46%

Topics — the 3 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Natural language and speech › Information extraction and text analysis text classification	0.1	2	2002	Text Classification using String Kernels · J. Mach. Learn. Res. 2002 Text Classification using String Kernels · NIPS 2000
Machine learning › Kernel, tree and ensemble methods › kernel methods › structured kernel string kernel	0.0	1	2000	Text Classification using String Kernels · NIPS 2000
Machine learning and data management kernel methods	0.0	1	2000	Text Classification using String Kernels · NIPS 2000

Methods — techniques the papers use, named apart from their topics

kernel methods · 0.1dynamic programming · 0.1support vector machine · 0.0

Year	Publication	Venue	Position
2013	Deep Relational Machines Huma Lodhi	ICONIP (2)	1
2011	Bootstrapping Parameter Estimation in Dynamic Systems Huma Lodhi, David R. Gilbert	Discovery Science	1
2009	Learning Large Margin First Order Decision Lists for Multi-Class Classification Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg	Discovery Science	1
2005	Support Vector Inductive Logic Programming Stephen H. Muggleton, Huma Lodhi, Ata Amini, Michael J. E. Sternberg	Discovery Science	2
2002	Boosting strategy for classification Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor	Intell. Data Anal.	1
2002	Latent Semantic Kernels Nello Cristianini, John Shawe-Taylor, Huma Lodhi	J. Intell. Inf. Syst.	3
2002	Text Classification using String Kernels Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins	J. Mach. Learn. Res.	1
2001	Latent Semantic Kernels Nello Cristianini, John Shawe-Taylor, Huma Lodhi	ICML	3
2000	Boosting the Margin Distribution Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor	IDEAL	1
2000	Text Classification using String Kernels abstract We introduce a novel kernel for comparing two text documents. The kernel is an inner product in the feature space consisting of all subsequences of length k. A subsequence is any ordered se(cid:173) quence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences which are close to contiguous. A direct compu(cid:173) tation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes how despite this fact the inner product can be efficiently evaluated by a dynamic programming technique. A preliminary experimental comparison of the performance of the kernel compared with a stan(cid:173) dard word feature space kernel results. [6] is made showing encouraging Huma Lodhi, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins	NIPS	1