Journal ArticleDOI
Instance labeling in semi-supervised learning with meaning values of words
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TLDR
Experimental results show that labeling unlabeled instances based on meaning scores of words to augment the training set is valuable, and increases the classification accuracy on previously unseen test instances significantly.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2017-06-01. It has received 12 citations till now. The article focuses on the topics: Semi-supervised learning & Supervised learning.read more
Citations
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Journal ArticleDOI
A recent overview of the state-of-the-art elements of text classification
TL;DR: Six baseline elements of text classification including data collection, data analysis for labelling, feature construction and weighing, feature selection and projection, training of a classification model, and solution evaluation are described.
Journal ArticleDOI
Learning document representation via topic-enhanced LSTM model
Wenyue Zhang,Yang Li,Suge Wang +2 more
TL;DR: This work introduces a latent topic modeling layer with similarity constraint on the local hidden representation of word sequence in a given document, and builds a tree-structured LSTM on top of the topic layer for generating semantic representation of the document.
Journal ArticleDOI
A safe screening rule for Laplacian support vector machine
Zhiji Yang,Yitian Xu +1 more
TL;DR: Different from most existing methods, the proposed safe screening rule for LapSVM (SSR-LapSVM) can effectively deal with the multiple parameter problems and has the safety, in the sense that the solution is exactly the same as the original LapS VM.
Proceedings ArticleDOI
Zero-Shot Classification of Biomedical Articles with Emerging MeSH Descriptors
TL;DR: This work proposes a new ML approach in the field of zero-shot classification, focused on classifying abstracts that come from PubMed, a well-known resource of publications from the biomedical field that uses bioBERT embeddings for transforming the textual data into a new semantic space exploiting them on sentence-level.
Journal ArticleDOI
Semi-supervised support vector regression based on data similarity and its application to rock-mechanics parameters estimation
Xi Chen,Xi Chen,Weihua Cao,Weihua Cao,Chao Gan,Chao Gan,Yasuhiro Ohyama,Jinhua She,Min Wu,Min Wu +9 more
TL;DR: A novel semi-supervised support vector machine soft sensor is devised considering the characteristics of the parameters and takes into account data similarity and selects labeled data set that are most similar to the continuous unlabeled data set at each iteration to improve estimation performance.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Book ChapterDOI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.