Using Prior Knowledge for Training Set Selection
Using Prior Knowledge in the Design of Classifiers
... utilizing prior knowledge to design better perform- ing classifiers when sample sizes are ...results using the Zipf model show that the proposed paradigm yields improved classifiers that outperform ...
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Using Training Set Selection Methods to Improve Market Prediction via News Headlines
... III. SYSTEM OVERVIEW/SYSTEM ARCHITECTURE Proposed method in this paper has three main steps. In the first step of proposed method, newsgroups are entered into pre- processing stage. This stage includes stop words remove, ...
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Working Set Selection Using Second Order Information for Training Support Vector Machines
... working set selection could reduce the number of iterations and hence are an important research ...that using second order information generally leads to faster ...these selection methods are ...
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Rough Set Feature Selection Using Bat Algorithm
... validated using domain knowledge or a validation ...Feature selection selects a subset of features from the original feature set without any transformation, and maintains the physical meanings ...
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Rough Set Feature Selection Using Bat Algorithm
... the training time, which led to increase network efficiency and reduce the fall errors and thus the algorithm is very efficient in multiple applications, such as image processing and clustering, ...
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An efficient instance selection algorithm to reconstruct training set for support vector machine
... 5.1. Datasets The experiments are carried out on eleven datasets includ- ing nine low dimensional and two high dimensional datasets. The low dimensional datasets are concisely introduced as follows. Glass comes from USA ...
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Survey on Rough Set Feature Selection Using Evolutionary Algorithm
... balanced training data, data imbalance presents a unique challenging problem to classifier design when the misclassification costs for the two classes are different ...feature selection is even more ...
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Identification of Mechatronic Systems with Dynamic Neural Networks using Prior Knowledge
... Figure 8: Output signals of the SDNN modell ˆ˙ ϕ 2 and the real TMS ˙ ϕ 2 with resulting cost function. 8 displays the outputs of the TMS and the SDNN-model during the identification process for the first set of ...
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Extraction of chemical-induced diseases using prior knowledge and textual information
... Two sets of linguistic features are used. For the first set, only one pair of chemical and disease mentions in the document is considered. The pair is selected on the basis of the following heuristics. A pair with ...
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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
... incorporating prior knowledge into NMT, how to combine multiple overlapping, arbitrary prior knowledge sources still remains a major ...to training objectives (Cohn et ...
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Using a human disease network for augmenting prior knowledge about diseases
... feature selection methods to reduce this number; at the same time, this technique ranks functions according to how relevant they are for prediction of disease ...
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Improved Bayesian Feature Selection and Classification Methods using Bootstrap Prior Techniques
... classifier using linear and quadratic discriminant analyses were updated with the application of bootstrap prior technique in the area of preliminary feature selection and estimation of parameters ...
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Sentiment Classification using Rough Set based Hybrid Feature Selection
... RSAR algorithm finds the vague attributes which do not have important role in the classification. Therefore, it is needed to remove redundant fea- tures without changing the knowledge embedded in the information ...
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Encoding Prior Knowledge with Eigenword Embeddings
... finally, using W IKI 5 gives an average of ...first set of ...adding prior knowledge to eigenword embed- dings does improve the quality of word vectors for the word similarity, geographic ...
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How to Set Cutoff Scores for Knowledge Tests Used In Promotion, Training, Certification, and Licensing
... What process can be used to identify “normal expectations of acceptable proficiency within the work force”? Those of us working in the testing and selection field look to the profession and find different ...
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Brain covariance selection: better individual functional connectivity models using population prior
... The focus of this work is the estimation of a large-scale Gaussian model to give a probabilistic description of brain functional signals. The difficulties are two-fold: on the one hand, there is a shortage of data to ...
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The Neural Network Selection for a Medical Diagnostic System using an Artificial Data Set
... network selection that works as a conclusion-making unit of walk-abnormalities ...network selection and training show how to avoid difficulties with limited num- ber of available data records, needed ...
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Using the Web as an Implicit Training Set: Application to Structural Ambiguity Resolution
... and knowledge-based approaches on a wide range of NLP ...computed using search engine statistics was not significantly differ- ent from the best supervised algorithm whose pa- rameters were tuned and which ...
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Towards a Dependency Parser for Greek Using a Small Training Data Set
... a training corpus, under the considerations exposed here, should reveal new problems to deal ...of training corpora for dependency parsing using small training data ...versus prior ...
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Automating Feature Set Selection for Case Based Learning of Linguistic Knowledge
... We apply the linguistic bias approach to feature set selection to the problem of relative pronoun disambiguation and show that the case- based learning algorithm [r] ...
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