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About 809 papers for “String kernel”
Tim Seifert, Andreas Lund, Benny Kneissl et al. · Bioinformatics · 2014
The SKINK web server is freely accessible at http://biows-inf.zdv.uni-mainz.de/skink. Moreover, SKINK is a module of the BALL software, also freely available at www.ballview.org.
Cited by 2
Živorad Tomovski, Trifce Sandev · Computers & Mathematics with Applications · 2011
Cited by 29
Changli Zhang, Wanli Zuo, Tao Peng et al. · 2008
Sentiment classification aims at mining reviews of people for a certain event's topic or product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers' opinions from the web information. Automatic opinion mining will benefit to both decision maker and ordinary people. Up to now, it is still a complicated task with great challenge. There are mainly two types of approaches for sentiment classification, machine learning methods and semantic orientation methods. Though some pioneer researches explored the approaches for English reviews classification, few jobs have been done on sentiment classification for Chinese reviews. The machine learning approach Based on string kernel for sentiment classification on reviews written in Chinese was proposed in this paper. Data experiment shows the capability of this approach.
Cited by 41
E. S. Elkhouly, M. Y. Abd‐Rabbou, S. I. Ali et al. · Europhysics Letters (EPL) · 2025
Abstract In this study, the quantum kernel is utilized as an indicator of the security of encoded information within classical strings. The impact of the Hadamard gate on the behavior of single, control, and all quantum strings is analyzed. To illustrate this concept, two distinct classical strings are examined. Our findings reveal that the position of the string significantly influences the level of security of the encoded information, depending on whether it acts as a control or target string. Furthermore, the quantum string's capacity remains almost stable when it functions as a target string during the entanglement process, whereas an unstable behavior is observed when it serves as the control string.
Cited by 0
Ryo Inoue, Hidehisa Nakayama, Nei Kato · 2006
In this paper, we propose a method to compose a classifier by non-linear discriminant analysis using kernel method combined with kernel feature selection for holistic recognition of historical hand-written string. Through experiments using historical hand-written string database HCD2, we show that our approach can obtain high recognition accuracy comparable to that of individual character recognition.
Cited by 1
James S. Magnuson, Heejo You, Thomas Hannagan · Journal of Cognition · 2024
The Time-Invariant String Kernel (TISK) model of spoken word recognition (Hannagan, Magnuson & Grainger, 2013; You & Magnuson, 2018) is an interactive activation model with many similarities to TRACE (McClelland & Elman, 1986). However, by replacing most time-specific nodes in TRACE with time-invariant open-diphone nodes, TISK uses orders of magnitude fewer nodes and connections than TRACE. Although TISK performed remarkably similarly to TRACE in simulations reported by Hannagan et al., the original TISK implementation did not include lexical feedback, precluding simulation of top-down effects, and leaving open the possibility that adding feedback to TISK might fundamentally alter its performance. Here, we demonstrate that when lexical feedback is added to TISK, it gains the ability to simulate top-down effects without losing the ability to simulate the fundamental phenomena tested by Hannagan et al. Furthermore, with feedback, TISK demonstrates graceful degradation when noise is added to input, although parameters can be found that also promote (less) graceful degradation without feedback. We review arguments for and against feedback in cognitive architectures, and conclude that feedback provides a computationally efficient basis for robust constraint-based processing.
Cited by 4Open access
M. Wang, Jianguang Yang, K.-C. Chou · Amino Acids · 2005
Cited by 1Open access
M. Wang, Jie Yang, K.-C. Chou · Amino Acids · 2005
Cited by 48
James S. Magnuson, Heejo You, Thomas Hannagan · 2023
The Time-Invariant String Kernel (TISK) model of spoken word recognition (Hannagan, Magnuson & Grainger, 2013; You & Magnuson, 2018) is an interactive activation model with many similarities to TRACE (McClelland & Elman, 1986). However, by replacing most time-specific nodes in TRACE with time-invariant open-diphone nodes, it uses orders of magnitude fewer nodes and connections than TRACE. Although TISK performed remarkably similarly to TRACE in simulations reported by Hannagan et al., the original TISK implementation did not include lexical feedback, precluding simulation of top-down effects, and leaving open the possibility that adding feedback to TISK might fundamentally alter its performance. Here, we demonstrate that when lexical feedback is added to TISK, it gains the ability to simulate top-down effects without losing the ability to simulate the fundamental phenomena tested by Hannagan et al. Furthermore, with feedback, TISK demonstrates graceful degradation when noise is added to input, although parameters without feedback can be found that also promote (less) graceful degradation. We review arguments for and against feedback in cognitive architectures, and argue that feedback provides a computationally efficient basis for robust constraint-based processing.
Cited by 0Open access
Robert P. Holley, Daniel Rosenfeld · 2010
We describe implementation of a classifier for User-Agent strings using Support Vector Machines. The best kernel is found to be the linear kernel, even when more complicated string based kernels, such as the edit distance kernel and the subsequence kernel, are employed. A robust tokenization scheme is employed which dramatically speeds up the calculation for the edit string and subsequence kernels by shortening the effective string length. 1
Cited by 3
Chandra Das, Pradipta Maji · 2008
In most pattern recognition algorithms, amino acids cannot be used directly as inputs since they are nonnumerical variables. They, therefore, need encoding prior to input. In this regard, a novel string kernel is introduced, which maps a nonnumerical sequence space to a numerical feature space.The proposed string kernel is developed based on the conventional bio-basis function and termed as novel bio-basis function. The novel bio-basis function is designed based on the principle of asymmetricity of biological distance, which is calculated using an amino acid mutation matrix. The concept of zone of influence of bio-basis is introduced in the proposed string kernel to normalize the asymmetric distance. An efficient method to select bio-bases for the novel string kernel is described integrating the concepts of the Fisher ratio and degree of resemblance. The effectiveness of the proposed string kernel and bio-bases selection method, along with a comparison with existing kernel and related selection methods, is demonstrated on different protein data sets.
Cited by 0
Pradipta Maji, Chandra Das · IEEE Transactions on NanoBioscience · 2010
In order to apply the powerful kernel-based pattern recognition algorithms such as support vector machines to predict functional sites in proteins, amino acids need encoding prior to input. In this regard, a new string kernel function, termed as the modified bio-basis function, is proposed that maps a nonnumerical sequence space to a numerical feature space. The proposed string kernel function is developed based on the conventional bio-basis function and needs a bio-basis string as a support like conventional kernel function. The concept of zone of influence of a bio-basis string is introduced in the proposed kernel function to take into account the influence of each bio-basis string in nonnumerical sequence space. An efficient method is described to select a set of bio-basis strings for the proposed kernel function, integrating the Fisher ratio and a novel concept of degree of resemblance. The integration enables the method to select a reduced set of relevant and nonredundant bio-basis strings.
Cited by 3
Ritambhara Singh, Arshdeep Sekhon, Kowsari, Kamran et al. · Figshare · 2017
This data record consists of supplementary information files, all datasets and code used in the ECML PKDD 2017 paper <b>GaKCo: a Fast Gapped k-mer string Kernel using Counting</b> (README included).<br>GaKCo is a fast and naturally parallelizable algorithm for gapped k-mer based string kernel calculation. GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is scalable to larger dictionary size and more number of mismatches.<br><b>GaKCo_ECML17_Supplementary.pdf</b> - presents schematics of the GakCo algorithm, formal proof regarding Hamming Distance Property, justification of GaKCo's Sort and Count Method, connections to previous studies, details of the datasets, an overview of empirical Performance of GaKCo versus Neural Networks and other related experiments.<br><b>data.zip</b> - contains 38 training and testing nucloetide and peptide datasets in <b>.fasta</b> format: a text-based format in which base pairs or amino acids are represented using single-letter codes. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater-than (">") symbol in the first column. Protein, DNA and text dictionaries are provided in <b>.txt</b> format. Both formats are openly accessible via text edit software.<br>**Datasets for GaKCo:** We perform 19 different classification tasks to evaluate the performance of GaKCo. These tasks belong to the discussed three categories: (1) TF binding site prediction (DNA dataset), (2) Remote Protein Homology prediction (protein dataset), and (3) Character-based English text classification (text dataset).<br><b>code.zip - </b>contains code files in C++ format: <b>.cpp</b>, <b>.h,</b> and bash file: <b>.sh</b> to compile GaKCo using the openMP g++ compiler, and to obtain kernel output using data files and dictionaries above. See below for further detail.<br><br>Compiling GaKCo (with openMP) : ```g++ -c GaKCo.cpp -o GaKCo -fopenmp```To get kernel output: ```GaKCo #User options: > 5, =(0,..,g-1), = 0 for single-thread/ 1 for multithread```Bash script to run end-to-end kernel calculation:```processing.sh<br><br><b>Background</b>String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (<i>Σ</i>) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to O(<i>Σ</i><sup>M</sup>). We propose a fast algorithm for calculating Gapped k-mer Kernel using Counting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger <i>Σ</i> and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the Σ<sup>M</sup> term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets).<br>
Cited by 0Open access
Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari et al. · Figshare · 2017
This data record consists of supplementary information files, all datasets and code used in the ECML PKDD 2017 paper <b>GaKCo: a Fast Gapped k-mer string Kernel using Counting</b> (README included).<br>GaKCo is a fast and naturally parallelizable algorithm for gapped k-mer based string kernel calculation. GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is scalable to larger dictionary size and more number of mismatches.<br><b>GaKCo_ECML17_Supplementary.pdf</b> - presents schematics of the GakCo algorithm, formal proof regarding Hamming Distance Property, justification of GaKCo's Sort and Count Method, connections to previous studies, details of the datasets, an overview of empirical Performance of GaKCo versus Neural Networks and other related experiments.<br><b>data.zip</b> - contains 38 training and testing nucloetide and peptide datasets in <b>.fasta</b> format: a text-based format in which base pairs or amino acids are represented using single-letter codes. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater-than (">") symbol in the first column. Protein, DNA and text dictionaries are provided in <b>.txt</b> format. Both formats are openly accessible via text edit software.<br>**Datasets for GaKCo:** We perform 19 different classification tasks to evaluate the performance of GaKCo. These tasks belong to the discussed three categories: (1) TF binding site prediction (DNA dataset), (2) Remote Protein Homology prediction (protein dataset), and (3) Character-based English text classification (text dataset).<br><b>code.zip - </b>contains code files in C++ format: <b>.cpp</b>, <b>.h,</b> and bash file: <b>.sh</b> to compile GaKCo using the openMP g++ compiler, and to obtain kernel output using data files and dictionaries above. See below for further detail.<br><br>Compiling GaKCo (with openMP) : ```g++ -c GaKCo.cpp -o GaKCo -fopenmp```To get kernel output: ```GaKCo #User options: > 5, =(0,..,g-1), = 0 for single-thread/ 1 for multithread```Bash script to run end-to-end kernel calculation:```processing.sh<br><br><b>Background</b>String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (<i>Σ</i>) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to O(<i>Σ</i><sup>M</sup>). We propose a fast algorithm for calculating Gapped k-mer Kernel using Counting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger <i>Σ</i> and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the Σ<sup>M</sup> term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets).<br>
Cited by 0Open access
Raul Torres, Julian Kunkel, Manuel F. Dolz et al. · Lecture notes in computer science · 2017
Cited by 1
N. E. J. Bjerrum-Bohr, Poul H. Damgaard, Thomas Søndergaard et al. · Journal of High Energy Physics · 2011
We derive an explicit formula for factorizing an n-point closed string amplitude into open string amplitudes. Our results are phrased in terms of a momentum kernel which in the limit of infinite string tension reduces to the corresponding field theory kernel. The same momentum kernel encodes the monodromy relations which lead to the minimal basis of color-ordered amplitudes in Yang-Mills theory. There are interesting consequences of the momentum kernel pertaining to soft limits of amplitudes. We also comment on surprising links between gravity and certain combinations of kinematic and color factors in gauge theory.
Cited by 260Open access
Bjerrum-Bohr, N. E. J., Damgaard, Poul H., Sondergaard, Thomas et al. · arXiv (Cornell University) · 2010
We derive an explicit formula for factorizing an $n$-point closed string amplitude into open string amplitudes. Our results are phrased in terms of a momentum kernel which in the limit of infinite string tension reduces to the corresponding field theory kernel. The same momentum kernel encodes the monodromy relations which lead to the minimal basis of color-ordered amplitudes in Yang-Mills theory. There are interesting consequences of the momentum kernel pertaining to soft limits of amplitudes. We also comment on surprising links between gravity and certain combinations of kinematic and color factors in gauge theory.
Cited by 0Open access
Dongsheng Cao, Jianchao Zhao, Yan-Ning Yang et al. · SAR and QSAR in environmental research · 2012
There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed models. The results obtained indicate that SVM based on the SMILES string kernel can be regarded as a very promising and alternative modelling approach for potential toxicity prediction of chemicals.
Cited by 46
Alexander K. Seewald, Florian Kleedorfer · Advances in Data Analysis and Classification · 2007
Cited by 10
Pradipta Maji, Chandra Das · IEEE Transactions on NanoBioscience · 2010
The prediction of functional sites in proteins is an important issue in protein function studies and drug design. To apply the kernel based pattern recognition algorithms such as support vector machines for protein functional sites prediction, a new string kernel function, termed as the modified bio-basis function, is proposed recently. The bio-basis strings for the new kernel function are selected by an efficient method that integrates the Fisher ratio and the concept of degree of resemblance. In this regard, this paper introduces some quantitative indices for evaluating the quality of selected bio-basis strings. Moreover, the effectiveness of the new string kernel function and bio-basis string selection method, along with a comparison with existing bio-basis function and related bio-basis string selection methods, is demonstrated on different protein data sets using the proposed quantitative indices and support vector machines.
Cited by 2