[PDF] Top 20 Text Classification using String Kernels
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Text Classification using String Kernels
... kernel using such features can be defined and calculated in a very efficient way by using dynamic programming ...as string subsequence kernel (SSK) is based on work (Watkins, 2000; Haussler, 1999) ... See full document
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Nonextensive Information Theoretic Kernels on Measures
... theoretic kernels, in com- parison with common kernels, for SVM-based text ...binary classification task that tries to discriminate among the two largest categories of each data set; this led ... See full document
41
Automated essay scoring with string kernels and word embeddings
... combine string kernels (low-level character n-gram features) and word embeddings (high-level semantic features) to obtain state-of-the-art AES ...on string kernels have demonstrated remarkable ... See full document
7
Comparison of Short Text Sentiment Analysis Methods for Croatian
... evaluated using the average of the F1-scores for the positive and the negative ...and string kernel models, using different feature sets and kernel ...obtained using stemming on all three ... See full document
7
Using String Kernels to Identify Performers from their Playing Style
... Partial Least Squares (PLS) was developed by Herman Wold during the 1960’s in the field of econometrics [10]. It offers an effective approach to solving problems with training data that has few points but high ... See full document
12
Fast String Kernels using Inexact Matching for Protein Sequences
... for string kernels for biological sequences and were designed, in particular, for the application of SVM protein ...These kernels use counts of common occurrences of short k-length subsequences, ... See full document
21
Domain Kernels for Text Categorization
... a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. This collection has become a popular data set for experiments in text appli- cations of machine ... See full document
8
Relation Classification using Entity Sequence Kernels
... create string sequences, which we call entity ...modified string kernel is applied over entity ...experiments using a shortest path dependency kernel similar to the one introduced by Bunescu and ... See full document
10
Character based String Kernels for Bio Entity Relation Detection
... Extracting bio-entity relations has emerged as an important task due to the ever-growing number of bio-medical doc- uments. In this paper, we present a simple and novel representation for extracting bio-entity ... See full document
6
Semantic Classification with Distributional Kernels
... Distributional kernels on strings and trees should provide a flexible implementation of these suggestions that is compatible with SVM classification and does not require manual feature ...to kernels ... See full document
8
Improving Text Classification by a Sense Spectrum Approach to Term Expansion
... S is an appropriately shaped matrix commonly re- ferred to as semantic smoothing matrix (Siolas and d’Alch´e Buc, 2000; Shawe-Taylor and Cristianini, 2004; Basili et al., 2005; Mavroeidis et al., 2005; Bloehdorn et al., ... See full document
9
Efficient kernels for sentence pair classification
... A very important class of graphs can be de- fined for tasks involving sentence pairs. In these cases, an important class of feature spaces is the one that represents first-order rewrite rules. For example, in textual ... See full document
10
String Kernels for Native Language Identification: Insights from Behind the Curtains
... The string kernels approach is evaluated on Arabic data in order to demonstrate that the approach is language independent by showing that it can obtain state-of-the-art ...the string kernel systems ... See full document
36
Single and Cross domain Polarity Classification using String Kernels
... Polarity Classification Following recent works in cross-domain polar- ity classification (Bollegala et ...art using a multi-source cross- domain setting, ...cross-domain classification ... See full document
6
Graph kernels and applications in protein classification
... theory[1]. Kernels help to adapt classification regardless how classification ...say, kernels act like an interface between clas- sification tools and data sets via Support Vector ...designing ... See full document
7
Learning Translation Invariant Kernels for Classification
... of kernels times the number of support ...cosine kernels, regardless of the number of support vectors. In Section 8, using a learning theory discussion, we show the necessity of controlling the ... See full document
38
008856 00 DOMAIN System Call Reference Vol2 Feb86 pdf
... If you are decoding ASOII text strings, you must provide two variables for each text string: a character string to contain the decoded string, and a 2-byte integer variable to contain th[r] ... See full document
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007196 01 Domain System Call Reference Jan87 pdf
... If you are decoding ASCII text strings, you must provide two variables for each text string: a character string to contain the decoded string, and a 2-byte integer variable to contain th[r] ... See full document
722
Bayesian Classification of High Dimensional Data with Gaussian Process using Different Kernels
... Gaussian processes attempt to use mean and covariance function in lieu of mean and covariance used in Gaussian distribution. Though support vector machine is a celebrated classifier (SVM), it is not specifically designed ... See full document
7
Community Kernels Detection in OSN using SVM Clustering and Classification
... community kernels in ...community kernels in large OSN using combinatorial method of support vector machine based clustering and classification of Community kernels in the dataset is ... See full document
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