3,801 research outputs found
Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information
In computing, spell checking is the process of detecting and sometimes
providing spelling suggestions for incorrectly spelled words in a text.
Basically, a spell checker is a computer program that uses a dictionary of
words to perform spell checking. The bigger the dictionary is, the higher is
the error detection rate. The fact that spell checkers are based on regular
dictionaries, they suffer from data sparseness problem as they cannot capture
large vocabulary of words including proper names, domain-specific terms,
technical jargons, special acronyms, and terminologies. As a result, they
exhibit low error detection rate and often fail to catch major errors in the
text. This paper proposes a new context-sensitive spelling correction method
for detecting and correcting non-word and real-word errors in digital text
documents. The approach hinges around data statistics from Google Web 1T 5-gram
data set which consists of a big volume of n-gram word sequences, extracted
from the World Wide Web. Fundamentally, the proposed method comprises an error
detector that detects misspellings, a candidate spellings generator based on a
character 2-gram model that generates correction suggestions, and an error
corrector that performs contextual error correction. Experiments conducted on a
set of text documents from different domains and containing misspellings,
showed an outstanding spelling error correction rate and a drastic reduction of
both non-word and real-word errors. In a further study, the proposed algorithm
is to be parallelized so as to lower the computational cost of the error
detection and correction processes.Comment: LACSC - Lebanese Association for Computational Sciences -
http://www.lacsc.or
An Experimental Evaluation of Nearest Neighbour Time Series Classification
Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~\cite{lines14elastic}, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting for -NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation
Learning Character Strings via Mastermind Queries, with a Case Study Involving mtDNA
We study the degree to which a character string, , leaks details about
itself any time it engages in comparison protocols with a strings provided by a
querier, Bob, even if those protocols are cryptographically guaranteed to
produce no additional information other than the scores that assess the degree
to which matches strings offered by Bob. We show that such scenarios allow
Bob to play variants of the game of Mastermind with so as to learn the
complete identity of . We show that there are a number of efficient
implementations for Bob to employ in these Mastermind attacks, depending on
knowledge he has about the structure of , which show how quickly he can
determine . Indeed, we show that Bob can discover using a number of
rounds of test comparisons that is much smaller than the length of , under
reasonable assumptions regarding the types of scores that are returned by the
cryptographic protocols and whether he can use knowledge about the distribution
that comes from. We also provide the results of a case study we performed
on a database of mitochondrial DNA, showing the vulnerability of existing
real-world DNA data to the Mastermind attack.Comment: Full version of related paper appearing in IEEE Symposium on Security
and Privacy 2009, "The Mastermind Attack on Genomic Data." This version
corrects the proofs of what are now Theorems 2 and 4
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size
The development of a satisfying and rigorous mathematical understanding of
the performance of neural networks is a major challenge in artificial
intelligence. Against this background, we study the expressive power of neural
networks through the example of the classical NP-hard Knapsack Problem. Our
main contribution is a class of recurrent neural networks (RNNs) with rectified
linear units that are iteratively applied to each item of a Knapsack instance
and thereby compute optimal or provably good solution values. We show that an
RNN of depth four and width depending quadratically on the profit of an optimum
Knapsack solution is sufficient to find optimum Knapsack solutions. We also
prove the following tradeoff between the size of an RNN and the quality of the
computed Knapsack solution: for Knapsack instances consisting of items, an
RNN of depth five and width computes a solution of value at least
times the optimum solution value. Our results
build upon a classical dynamic programming formulation of the Knapsack Problem
as well as a careful rounding of profit values that are also at the core of the
well-known fully polynomial-time approximation scheme for the Knapsack Problem.
A carefully conducted computational study qualitatively supports our
theoretical size bounds. Finally, we point out that our results can be
generalized to many other combinatorial optimization problems that admit
dynamic programming solution methods, such as various Shortest Path Problems,
the Longest Common Subsequence Problem, and the Traveling Salesperson Problem.Comment: A short version of this paper appears in the proceedings of AAAI 202
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
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