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Resolving Dependency Ambiguity Of Subordinate Clauses Using Support Vector Machines

Sang-Soo Kim, Seong-Bae Park, Sang-Jo Lee
2007 Zenodo  
In this paper, we propose a method of resolving dependency ambiguities of Korean subordinate clauses based on Support Vector Machines (SVMs).  ...  In order to solve this problem, we assume that the dependency relation of Korean subordinate clauses is the dependency relation among verb phrase, verb and endings in the clauses.  ...  Section 2 surveys the previous work on clause recognition and analysis of inter-clause relation, and Section 3 introduces how a support vector machine works which is adopted as a base learner for the task  ... 
doi:10.5281/zenodo.1084641 fatcat:6psjnofburhyndkxtwebehky7m

Page 8516 of Mathematical Reviews Vol. , Issue 2002K [page]

2002 Mathematical Reviews  
Support vector machines deal with learning algorithms involved in pattern recognition problems.  ...  In the soft-boundary/soft-margin case, the improvement over support vector machines is shown to be reduced.  ... 

Problems of the Automatic Emotion Recognitions in Spontaneous Speech; An Example for the Recognition in a Dispatcher Center [chapter]

Klára Vicsi, Dávid Sztahó
2011 Lecture Notes in Computer Science  
Numerous difficulties, in the examination of emotions occurring in continuous spontaneous speech, are discussed in this paper, than different emotion recognition experiments are presented, using clauses  ...  Summing up the results of these experiments, we can say, that clauses can be an optimal unit of the recognition of emotions in continuous speech.  ...  We would like to thank the leader of the SPSS Hungary Ltd. and INVITEL Telecom Zrt. to give free run of the recorded 1000 dialogues for us.  ... 
doi:10.1007/978-3-642-18184-9_28 fatcat:fnf4iz66nnbsroischwpdloi6m

A New Kind of Finite-State Automaton: Register Vector Grammar

Glenn D. Blank
1985 International Joint Conference on Artificial Intelligence  
RVG is functionally complex: ternary feature vectors (e.g. +-±--++) match and change by masking ( + matches but does not change any value).  ...  Register Vector Grammar is a new kind of finite-state automaton that is sensitive to context-without, of course, being contextsensitive in the sense of Chomsky hierarchy.  ...  Just as several boundary productions (SUBJ, PREP, etc.) suspend further clause-level productions while enabling NP, so other boundary productions may shift attention from one clause level to another.  ... 
dblp:conf/ijcai/Blank85 fatcat:xdfhnckwevgcznjspvte3xidce

First-Order Rule Induction for the Recognition of Morphological Patterns in Topographic Maps [chapter]

D. Malerba, F. Esposito, A. Lanza, F. A. Lisi
2001 Lecture Notes in Computer Science  
Research issues related to the extraction of first-order logic descriptions from vectorized topographic maps are introduced.  ...  Nevertheless, the acquisition of the necessary knowledge is still an open problem to which machine learning techniques can provide a solution.  ...  Empowering GIS with advanced pattern recognition capabilities would support effectively map readers in map interpretation tasks.  ... 
doi:10.1007/3-540-44596-x_8 fatcat:6ia2rsf4pjechel3cmibbb3qxq

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications [article]

Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen
2018 arXiv   pre-print
Our empirical comparison with Na\"ive Bayes, decision trees, linear support vector machines (SVMs), random forest, long short-term memory (LSTM) neural networks, and other techniques, is quite conclusive  ...  The Tsetlin Machine learns these formulae from a labelled text, utilizing conjunctive clauses to represent the particular facets of each category.  ...  Simpler techniques such as Naïve Bayes, logistic regression, decision trees, random forest, k-nearest neighbors (kNN), and Support Vector Machine (SVM) are still widely used, arguably because they are  ... 
arXiv:1809.04547v2 fatcat:6vjj734ml5aahd36x5nmelobi4

Online Learning via Global Feedback for Phrase Recognition

Xavier Carreras, Lluís Màrquez
2003 Neural Information Processing Systems  
Experimentation on a syntactic parsing problem, the recognition of clause hierarchies, improves state-of-the-art results and evinces the advantages of our global training method over optimizing each function  ...  We provide a recognition-based feedback rule which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons.  ...  Xavier Carreras is supported by a grant from the Catalan Research Department.  ... 
dblp:conf/nips/CarrerasM03 fatcat:adpx4eppsfegbirkls76rznhhi


Ebrahim Zaheri Abdevand, Islamic Azad University, Ashtian, Iran, Shamsollah Ghanbari, Zhanat Umarova, Zhalgasbek Iztayev, South Kazakhstan State University, Shymkent, Kazakhstan
2019 Azerbaijan Journal of High Performance Computing  
They are high-end and will become one of the most used and important communication tools in the IT world in the future.  ...  Computer networks are difficult to use due to the large number of devices such as router, switch, hop, and many sophisticated security management protocols, but in networks defined with integrated management  ...  Data classifi cation by SVM SVM support vector machine is a learning process based on statistical learning theory, which is one of the best machine learning methods used in data mining.  ... 
doi:10.32010/26166127.2019. fatcat:xu6kx76xz5fdnk6jrnx4eed7qq

The Tsetlin Machine – A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic [article]

Ole-Christoffer Granmo
2021 arXiv   pre-print
To eliminate the longstanding problem of vanishing signal-to-noise ratio, the Tsetlin Machine orchestrates the automata using a novel game.  ...  In this paper, we introduce the Tsetlin Machine, which solves complex pattern recognition problems with propositional formulas, composed by a collective of Tsetlin Automata.  ...  Code Availability Source code and datasets for the Tsetlin Machine, available under the MIT Licence, can be found at and  ... 
arXiv:1804.01508v15 fatcat:ssmvlls2xfdjtbiagwxr5vq6hy

Filtering-Ranking Perceptron Learning for Partial Parsing

A. Xavier Carreras, B. Lluís Màrquez, C. Jorge Castro
2005 Machine Learning  
This work introduces a general phrase recognition system based on perceptrons, and a global online learning algorithm to train them together.  ...  A recognition-based feedback rule is presented which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons.  ...  Xavier Carreras was supported by a pre-doctoral grant from the Catalan Research Department. This publication only reflects the authors' views.  ... 
doi:10.1007/s10994-005-0917-x fatcat:xk5fpnilhbg4loaiow4hvgdz6m

Prediction of Learning Disabilities in School Age Children Using Decision Tree [chapter]

M. David Julie, Balakrishnan Kannan
2010 Communications in Computer and Information Science  
This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications  ...  In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.  ...  Support Vector Machines select a small number of critical boundary instances called support vectors from each class and build a linear discriminant function that separates them as widely as possible [  ... 
doi:10.1007/978-3-642-14493-6_55 fatcat:clsfryccfbg67p7v26ykdaffl4

A Multi-class Pattern Recognition Method by Combined Use of Multinomial Logit Model and K-Nearest Neighbor Rule

Osamu Hasegawa, Takio Kurita
2002 IAPR International Workshop on Machine Vision Applications  
Therefore, first we train MLM using the training vectors, and then apply K-NN to the output of the MLM.  ...  Consequently, we obtained the following recognition rates: (1) 36classes =. 100.0%, and (2) 82-classes =. 99.93%.  ...  The Weight Decay can be summarized as a clause as follows. We applied the clause to the Equ. (8) . By this, it is expected that the decision boundaries become clearer, too.  ... 
dblp:conf/mva/HasegawaK02 fatcat:zxne3tr32fcahgl3ke3smrpjk4

A Comparison of Statistical and Rule-Induction Learners for Automatic Tagging of Time Expressions in English

Jordi Poveda, Mihai Surdeanu, Jordi Turmo
2007 14th International Symposium on Temporal Representation and Reasoning (TIME'07)  
In this paper, we explain the knowledge representation used and compare the results obtained in our experiments with two different methods, one statistical (support vector machines) and one of rule induction  ...  Our aim is to explore the possibilities afforded by applying machine learning techniques to the recognition of time expressions.  ...  The first method considered is statistical and uses Support Vector Machines ( [11] ) as the underlying machine learning algorithm.  ... 
doi:10.1109/time.2007.38 dblp:conf/time/PovedaST07 fatcat:xsrtmgwppbaitdcdbvnzbepmci

Automatic Recognition of Narrative Drama Units: A Structured Learning Approach

Danilo Croce, Roberto Basili, Vincenzo Lombardo, Eleonora Ceccaldi
2019 European Conference on Information Retrieval  
The automatic identification of such elements in a drama is the first step in the recognition of their evolution, both at coarse and fine grain text level.  ...  We propose a generative inductive machine learning framework, combining Hidden Markov models and SVM and discuss the role of event information (thus involving agents and actions) at the lexical and grammatical  ...  A Markovian Support Vector Machine The aim of a Markovian formulation of SVM is to make the classification of a input example x i ∈ R n (belonging to a sequence of examples) dependent on the labels assigned  ... 
dblp:conf/ecir/CroceBLC19 fatcat:vjv6pnlvbzdilog277pmcgfg3q

TimeML-Compliant Text Analysis for Temporal Reasoning

Branimir Boguraev, Rie Kubota Ando
2005 International Joint Conference on Artificial Intelligence  
We address this problem with a hybrid TimeML annotator, which uses cascaded finite-state grammars (for temporal expression analysis, shallow syntactic parsing, and feature generation) together with a machine  ...  learning component capable of effectively using large amounts of unannotated data.  ...  (We use the same scheme for SIGNAL recognition.)  ... 
dblp:conf/ijcai/BoguraevA05 fatcat:rjcfavayhbfjzfmzmmycuaa26q
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