F-measure
On the Bayes-Optimality of F-Measure Maximizers
... Our theoretical results are specifically relevant for applications in multi-label classi- fication and structured output prediction. In these application domains, three different aggregation schemes of the ...
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AN INTERACTIVE FORM APPROACH FOR DATABASE QUERIES THROUGH F-MEASURE
... calculate F-measure of those attributes and update the query form by adding those ...expected F-Measure for measuring the goodness of a query ...
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Yet Another Symmetrical and Real-time Word Alignment Method: Hierarchical Sub-sentential Alignment using F-measure
... In this paper, we propose a novel method based on the use of F-measure for symmetrization of word alignment, at the same time which can be regarded as an real-time word alignment approach. We jus- tify this ...
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Expected F Measure Training for Shift Reduce Parsing with Recurrent Neural Networks
... In this paper, we present a global neural net- work parsing model, optimized for a task-specific loss based on expected F-measure. The model natu- rally incorporates beam search during training, and is ...
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Weighted maximum likelihood loss as a convenient shortcut to optimizing the F-measure of maximum entropy classifiers
... for F measure optimization for a partic- ular parametrization involving m 2 + 1 parameters where m is the number of examples in the binary classification ...expected F mea- sure using the link to the ...
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Evaluating F Measure Metric Using ManTra Machine Translation Engine in Tourism domain for English to Hindi Language
... English is understood by less than 3% of Indian population. Hindi, which is official language of the country, is used by more than 400 million people. MT assumes a much greater significance in breaking the language ...
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F Measure Metric for English to Hindi Language Machine Translation
... The present research work aims at studying the “Evaluation of Machine Translation Evaluation’s F-Measure metric for English to Hindi” for tourism domain. The present research work is the study of ...
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Maximum Expected F Measure Training of Logistic Regression Models
... In this paper we focus on binary classification tasks, and in particular on the loss or utility associ- ated with classification decisions. The three prob- lems mentioned before – prepositional phrase at- tachment, ...
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Query Recommender System using Fragments and Projection values Based F-measure and MongoDB
... Figure5 shows system employs the top-k fragments of previous queries in order to generate recommendations, show the effect of the choice of k on the average precision and F-score for the recommendations. I notice ...
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Coding and Indexing Shape Feature using Golomb-Rice Coding for CBIR Applications
... The effectiveness of the proposed encoded histogram is evaluated in an image retrieval system. For our experiment, we have used label me benchmark dataset (http://www.cucl.mit.edu/database.html) with 9356 images in which ...
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Machine Translation of Natural Language using different Approaches
... (R), F-measure (F) and METEOR (M) for randomly selected English sentences of all three categories of sentence by RBMT and EBMT in percentage has given in following table 1, ...
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An Efficient Fuzzy Data Clustering Algorithm for Relational Databases
... This algorithm produces good results for categorical attributes of relational databases. The results have clearly proven that the proposed algorithm produces better results than Fuzzy k Modes in terms of parameters ...
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A New Feature Selection Technique Combined with ELM Feature Space for Text Classification
... for experimental purpose. The classifiers which are used for comparison purpose are Support Vec- tor Machine (LinearSVC), Decision Tree (DT), SVM linear kernel (LinearSVM), Gaussian Naive Bayes (GNB), Random Forest (RF), ...
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Exploring Instances for Matching Heterogeneous Database Schemas Utilizing Google Similarity and Regular Expression
... We used real-world data sets from two different domains: Restaurant and Census, both of which are available online [36][37]. Table 4 shows the Characteristics of data sets. For comparison purpose, we compared our ...
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DEEP NEURAL CLASSIFICATION AND LOGIT REGRESSION BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK
... Ontology-based IR evaluation, we prepared ten Indonesian queries as examples shown in Table 4. We placed the appropriate keyword queries for use in the evaluation. Then, calculate the number of correct documents to be ...
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Identifying Patterns for Unsupervised Grammar Induction
... and F-measure obtained per constituent ...corresponding F-measure, are quite sim- ilar for every constituent ...high F-measure thanks to a very high recall but with a poor ...
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Learning to improve medical decision making from imbalanced data without a priori cost
... Table 3 summarizes the performance comparison among AdaBoost, Cost-sensitive decision tree, AdaCost, and our method RankCost with respect to three measures and their 95% confidence intervals. The results shown in Table 3 ...
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Classification of Disease Data Using Bijective Soft Set
... Classification is a data mining technique used to predict group membership for data instances. The classification algorithm learns from the training set and builds a model. The model is used to classify new objects. In ...
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Online Word Alignment for Online Adaptive Machine Translation
... A hot task in the Computer Assisted Translation scenario is the integration of Machine Translation (MT) systems that adapt sentence after sentence to the post- edits made by the translators. A main role in the MT online ...
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Building a System based on Intelligent Agencies for Assisting in Classification and Circulation of Books and Periodicals Automatically
... S.N. Bharath Bhushan, et .al has proposed an active likeness be made to calculate the approximation of two sets of text documents. Also, a similar pattern compression standard for text documents was suggested. It was ...
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