Probabilistic grammatical model for helix‐helix contact site classification
نویسندگان
چکیده
منابع مشابه
Probabilistic Tree Transducers for Grammatical Error Correction
We investigate the application of weighted tree transducers to correcting grammatical errors in natural language. Weighted finite-state transducers (FST) have been used successfully in a wide range of natural language processing (NLP) tasks, even though the expressiveness of the linguistic transformations they perform is limited. Recently, there has been an increase in the use of weighted tree ...
متن کاملGrammatical Error Correction as Multiclass Classification with Single Model
This paper describes our system in the shared task of CoNLL-2013. We illustrate that grammatical error detection and correction can be transformed into a multiclass classification task and implemented as a single-model system regardless of various error types with the aid of maximum entropy modeling. Our system achieves the F1 score of 17.13% on the standard test set.
متن کاملCredit Classification Using Grammatical Evolution
Grammatical Evolution (GE) is a novel data driven, model induction tool, inspired by the biological geneto-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to model the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data and the associated Stand...
متن کاملEfficient model selection for probabilistic K nearest neighbour classification
ProbabilisticK-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, K. The contribution of this paper is to incorporate t...
متن کاملA Linear Bayesian Updating Model for Probabilistic Spatial Classification
Abstract: Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classifica...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Algorithms for Molecular Biology
سال: 2013
ISSN: 1748-7188
DOI: 10.1186/1748-7188-8-31