Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Pavel, P Kuksa"'
Autor:
Pavel P. Kuksa
Publikováno v:
IEEE/ACM transactions on computational biology and bioinformatics. 10(5)
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequenc
Autor:
Vladimir Pavlovic, Pavel P. Kuksa
Publikováno v:
KDD
Classification of sequences drawn from a finite alphabet using a family of string kernels with inexact matching (e.g., spectrum or mismatch) has shown great success in machine learning. However, selection of optimal mismatch kernels for a particular
Autor:
Pavel P. Kuksa
Publikováno v:
BIOKDD
String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on tasks such as document topic elucidation, biological sequence classi
Autor:
Vladimir Pavlovic, Pavel P. Kuksa
Publikováno v:
ICPR
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, explo
Publikováno v:
BIBM
Recent studies in protein sequence analysis have leveraged the power of unlabeled data. For example, the profile and mismatch neighborhood kernels have shown significant improvements over classifiers estimated under the fully supervised setting. In t
Autor:
Vladimir Pavlovic, Pavel P. Kuksa
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540741251
WABI
WABI
In this work we study string kernel methods for sequence analysis and focus on the problem of species-level identification based on short DNA fragments known as barcodes. We introduce efficient sorting-based algorithms for exact string k-mer kernels
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0add555e38c6760d830c6617c9a7b002
https://doi.org/10.1007/978-3-540-74126-8_22
https://doi.org/10.1007/978-3-540-74126-8_22