Research topic

Algorithms and Data Compression

Discover papers and researchers connected with this scholarly topic.

Research papers

2001 · ScholarlyCommons (University of Pennsylvania) · 12,994 citations

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

We present Conditional Random Fields, a framework\nfor building probabilistic models to segment\nand label sequence data. Conditional random\nfields offer several advantages over hidden\nMarkov models and stochastic grammars\nfor such tasks, including the ability to relax\nstrong independence assumptions made in those\nmodels. Conditional random fields also avoid\na fundamental limitation of maximum entropy\nMarkov models (MEMMs) and other discriminative\nMarkov models based on directed graphical\nmodels, which can be biased towards states\nwith few successor states. We present iterative\nparameter estimation algorithms for conditional\nrandom fields and compare the performance of\nthe resulting models to HMMs and MEMMs on\nsynthetic and natural-language data.

1995 · 10,744 citations

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on arti cial data and theoretical results in restricted settings have shown that for selecting a good classi er from a set of classiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment| over half a million runs of C4. 5 and a Naive-Bayes algorithm| to estimate the e ects of di erent parameters on these algorithms on real-world datasets. For crossvalidation, we vary the number of folds and whether the folds are strati ed or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold strati ed cross validation, even if computation power allows using more folds.

1993 · 7,539 citations

Building a Large Annotated Corpus of English: The Penn Treebank

As a result of this grant, the researchers have now published oil CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, with over 3 million words of that material assigned skeletal grammatical structure. This material now includes a fully hand-parsed version of the classic Brown corpus. About one half of the papers at the ACL Workshop on Using Large Text Corpora this past summer were based on the materials generated by this grant.

1998 · Bioinformatics · 5,837 citations

Profile hidden Markov models.

The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.

1997 · 3,544 citations

Algorithms on strings, trees, and sequences computer science and computational biology

Part I. Exact String Matching: The Fundamental String Problem: 1. Exact matching: fundamental preprocessing and first algorithms 2. Exact matching: classical comparison-based methods 3. Exact matching: a deeper look at classical methods 4. Semi-numerical string matching Part II. Suffix Trees and their Uses: 5. Introduction to suffix trees 6. Linear time construction of suffix trees 7. First applications of suffix trees 8. Constant time lowest common ancestor retrieval 9. More applications of suffix trees Part III. Inexact Matching, Sequence Alignment and Dynamic Programming: 10. The importance of (sub)sequence comparison in molecular biology 11. Core string edits, alignments and dynamic programming 12. Refining core string edits and alignments 13. Extending the core problems 14. Multiple string comparison: the Holy Grail 15. Sequence database and their uses: the motherlode Part IV. Currents, Cousins and Cameos: 16. Maps, mapping, sequencing and superstrings 17. Strings and evolutionary trees 18. Three short topics 19. Models of genome-level mutations.