Machine Learning Models Evaluation
Performance Evaluation of Machine Learning Models for Diabetes Prediction
... classification models, that can anticipate the probability of disease in patients with the greatest ...and machine learning classifiers are used to the predict presence of disease ...classification ...
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Essay evaluation with machine learning
... of machine learning for building models for automated essay scoring and to evaluate the success of deep neural networks compared to other ...building models we have used already extracted ...
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Making machine learning models interpretable
... nonlinear models can have locally linear ...adequate evaluation measures to assess their ...nonlinear models is shown in [31], where Fuzzy-Supervised SOM (FSSOM), a semi-supervised variant of SOM ...
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A Machine Learning Approach to the Automatic Evaluation of Machine Translation
... 2 Data Our corpus consists of 350,000 aligned Spanish- English sentence pairs taken from published computer software manuals and online help documents. We extracted 200,000 English sentences for building language ...
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Technical note: Evaluation of three machine learning models for surface ocean CO 2mapping
... They have a circular property and therefore cannot be used directly. For instance, longitude −180 ◦ is geographically connected to longitude 180 ◦ , but numerically they appear to be two extreme longitude values to the ...
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An Evaluation of Hybrid Machine Learning Classifier Models for Identification of Terrorist Groups in the Aftermath of an Attack
... using machine learning ...build machine learning ...evaluate models. The models are evaluated on various parameters like classification accuracy, classification error and Build ...
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Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
... vector machine (SVM) is a supervised learning model that was conceptualized in the in 1960s for classifications problems and later extended to regression analysis (Basak et ...
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Learning Deep Transformer Models for Machine Translation
... For evaluation, we first average the last 5 check- points, each of which is saved at the end of an epoch. And then we use beam search with a beam size of 4/6 and length penalty of 0.6/1.0 for En- De/Zh-En tasks ...
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Screening for Prediabetes Using Machine Learning Models
... two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external ...
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Towards Interpretability and Robustness of Machine Learning Models
... CHAPTER 6. A QUERY-EFFICIENT DECISION-BASED ATTACK 78 It is more realistic to evaluate the vulnerability of a machine learning system under the decision-based attack with a limited budget of model queries. ...
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Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research
... Reynel-Ávila et al. [59] developed an innovative hybrid non-random two-liquid- ANN method in order to increase the estimation performance of the liquid-liquid equi- libria, which is used to simulate the biofuel process. ...
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A Framework for the Design and Evaluation of Machine Learning Applications
... these models exhibited imbalance, biasing them against some gender, socioeconomic, and racial groups (Seyyed- Kalantari et ...the models under-diagnosed individuals in groups that were ...
Activity recognition evaluation via machine learning
... The J48 algorithm has been used extensively in a wide range of research applications, including analysing e- governance data [61], mining software repositories [62], predicting fish stocks [63] and fault analysis [64] to ...
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User Interface Evaluation with Machine Learning Methods
... II evaluation model, like other simulation models, generates prediction results in addition to producing evaluation ...II evaluation model is the trace of interaction in time domain for a ...
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Machine Learning Models for Political Video Advertisement Classification
... Grant Schindler et al. describes the “bag of words” model to represent a video using different combinations of spatial and temporal descriptors [44]. We will use “Bag of words” representation in our text based feature ...
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Runtime Optimizations for Tree-Based Machine Learning Models
... in learning to rank, we believe that there are many optimiza- tion opportunities that take advantage of the fact that we need to evaluate multiple test instances in order to gen- erate a document ...
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Exploring Machine Learning Models for Wind Speed Prediction
... 2.6 Evaluation metrics The models with simple structure provide better generalization ability, but it may not be easy to learn the ...structure models learning can be done easily, but it is ...
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A Review of Machine Learning Models in the Air Quality Research
... The measurements used for performance evaluation are firstly the accuracy and secondly the root means square error (RMSE) for the prediction. Their study shows that the use of various features with M5P algorithm ...
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Development and Interpretation of Machine Learning Models for Drug Discovery
... Through iterative random removal of 50 % of the activity data from this profiling matrix, a sparsely populated high- dimensional matrix was obtained, hence presenting a chal- lenging prediction task. Through matrix ...
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Semantic Models for Machine Learning
... data learning using kernel ...alternative learning to non-linear functions by projecting the data into a high dimensional feature space in order to increase the power of linear learning ...
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