Towards multi-class detection: a self-learning approach to reduce inter-class noise from training dataset

2019 ◽  
Author(s):  
Amir Ghahremani ◽  
Egor Bondarev ◽  
Peter H. N. de With
Keyword(s):  
Training Dataset ◽  
Learning Approach ◽  
Class Noise ◽  
Self Learning

scholarly journals Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

2020 ◽  
Author(s):  
Widodo Budiharto ◽  
Vincent Andreas ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Humanoid Robot ◽  
Question Answering ◽  
Training Dataset ◽  
Learning Approach ◽  
Exact Match ◽  
Learning Capability ◽  
Question Answering Systems ◽  
Self Learning

Abstract The development of intelligent Humanoid Robot focuses on question answering systems to be able to interact with people is very rare. In this research, we would like to propose a Humanoid Robot with the self-learning capability for accepting and giving a response from people based on Deep Learning and big data from the internet. This kind of robot can be used widely in hotels, universities and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action, where the question from the user will be processed using deep learning, and the result will be compared with knowledge on the system. We proposed our deep learning approach, based on use GRU/LSTM, CNN and BiDAF with big data SQuAD as training dataset. Our experiment indicates that using GRU/LSTM encoder with BiDAF gives higher Exact Match and F1 Score, than CNN with the BiDAF model.

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates

Synlett ◽  
2020 ◽  
Author(s):  
Akira Yada ◽  
Kazuhiko Sato ◽  
Tarojiro Matsumura ◽  
Yasunobu Ando ◽  
Kenji Nagata ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Reaction Rates ◽  
Initial Reaction Rate ◽  
Training Dataset ◽  
Initial Reaction ◽  
Learning Approach ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Reasonable Prediction ◽  
Epoxidation Of Alkenes

AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.

Semisupervised sentiment analysis method for online text reviews

2020 ◽  
pp. 016555152091003
Author(s):  
Gyeong Taek Lee ◽  
Chang Ouk Kim ◽  
Min Song
Keyword(s):  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Model Space ◽  
Training Dataset ◽  
Learning Approach ◽  
Learning Models ◽  
Text Data ◽  
Learning Techniques ◽  
Sentiment Dictionary

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it requires lengthy time periods and high costs to build a reliable dictionary. The supervised learning approach uses machine learning models to learn the sentiment scores of words; however, training a classifier model requires large amounts of labelled text data to achieve a good performance. In this article, we propose a semisupervised approach that performs well despite having only small amounts of labelled data available for training. The proposed method builds a base sentiment dictionary from a small training dataset using a lasso-based ensemble model with minimal human effort. The scores of words not in the training dataset are estimated using an adaptive instance-based learning model. In a pretrained word2vec model space, the sentiment values of the words in the dictionary are propagated to the words that did not exist in the training dataset. Through two experiments, we demonstrate that the performance of the proposed method is comparable to that of supervised learning models trained on large datasets.

scholarly journals A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 739 ◽  
Author(s):  
Seung-Jun Shin ◽  
Young-Min Kim ◽  
Prita Meilanitasari
Keyword(s):  
Machine Tool ◽  
Process Planning ◽  
Predictive Modeling ◽  
Manufacturing Systems ◽  
Minimum Energy ◽  
Hybrid Learning ◽  
Prototype System ◽  
Learning Approach ◽  
Machining Processes ◽  
Self Learning

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

scholarly journals Cooperative Hybrid Semi-Supervised Learning for Text Sentiment Classification

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 133 ◽  
Author(s):  
Yang Li ◽  
Ying Lv ◽  
Suge Wang ◽  
Jiye Liang ◽  
Juanzi Li ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Ensemble Classifier ◽  
Sentiment Classification ◽  
Training Dataset ◽  
Support Vector ◽  
Seed Selection ◽  
Training Strategy ◽  
Whole Process ◽  
Self Learning

A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification. However, manually constructing such a training dataset with sentiment labels is a labor-intensive and time-consuming task. Therefore, based on the idea of effectively utilizing unlabeled samples, a synthetical framework that covers the whole process of semi-supervised learning from seed selection, iterative modification of the training text set, to the co-training strategy of the classifier is proposed in this paper for text sentiment classification. To provide an important basis for selecting the seed texts and modifying the training text set, three kinds of measures—the cluster similarity degree of an unlabeled text, the cluster uncertainty degree of a pseudo-label text to a learner, and the reliability degree of a pseudo-label text to a learner—are defined. With these measures, a seed selection method based on Random Swap clustering, a hybrid modification method of the training text set based on active learning and self-learning, and an alternately co-training strategy of the ensemble classifier of the Maximum Entropy and Support Vector Machine are proposed and combined into our framework. The experimental results on three Chinese datasets (COAE2014, COAE2015, and a Hotel review, respectively) and five English datasets (Books, DVD, Electronics, Kitchen, and MR, respectively) in the real world verify the effectiveness of the proposed framework.

scholarly journals Analyses and Modeling of Neural Machine Translation for English-to-Khasi

2020 ◽  
Vol 9 (2) ◽  
pp. 115-118
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Barrier ◽  
Attention Mechanism ◽  
Training Dataset ◽  
Learning Approach ◽  
Neural Machine Translation ◽  
Community Or ◽  
North East

Language barrier is a common issue faced by humans who move from one community or group to another. Statistical machine translation has enabled us to solve this issue to a certain extent, by formulating models to translate text from one language to another. Statistical machine translation has come a long way but they have their limitations in terms of translating words that belongs to an entirely different context that is not available in the training dataset. This has paved way for neural Machine Translation (NMT), a deep learning approach in solving sequence to sequence translation. Khasi is a language popularly spoken in Meghalaya, a north-east state in India. Its wide and unexplored. In this paper we will discuss about the modeling and analyzing of a NMT base model and a NMT model using Attention mechanism for English to Khasi.

Fundamentals of Automation Engineering: A hybrid project-based learning approach

2020 ◽  
pp. 002072092092846
Author(s):  
Devika Kataria ◽  
Gustavo Sanchez ◽  
Siddhartan Govindasamy
Keyword(s):  
Material Handling ◽  
Low Cost ◽  
Project Based Learning ◽  
Learning Approach ◽  
Successful Completion ◽  
Organization Learning ◽  
Control Functions ◽  
Library Resources ◽  
Three Stages ◽  
Self Learning

This paper describes authors’ experience with designing and teaching a new course on “Fundamentals of Automation Engineering”, based on a hybrid Project-Based Learning approach. The proposed challenge for students was to develop a low-cost Material Handling Machine, equipped with basic control functions. The project was executed in three stages, with different activities/tasks performed during each stage, which finally led to successful completion of the challenge. Detailed course file containing syllabus, learning objectives, assessment rubric, project goals, and deliverables was provided to students at first stage. Handouts and assignments were also provided weekly to students, which required extensive use of library resources (self-learning). Feedback survey was conducted fortnightly and suggestions were considered for course improvement. In this paper, authors discuss course organization, learning and assessment activities, and perception of students.

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