scholarly journals Supervised Learning Based Hypothesis Generation from Biomedical Literature

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shengtian Sang ◽  
Zhihao Yang ◽  
Zongyao Li ◽  
Hongfei Lin
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Supervised Learning ◽  
Biomedical Literature ◽  
Hypothesis Generation ◽  
Experimental Results ◽  
Learning Method ◽  
Abc Model ◽  
Useful Knowledge ◽  
Traditional Processing

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.

scholarly journals Application of Sensors in Intelligent Clothing Design

2018 ◽  
Vol 14 (06) ◽  
pp. 4
Author(s):  
Shali Jiang ◽  
Qiong Ren
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Clothing Design ◽  
Artificial Intelligence Technology ◽  
Contact Sensor ◽  
Intelligent Clothing ◽  
Contact Sensors

<p class="0abstract"><span lang="EN-US">In order to study the application of sensors in intelligent clothing design, the artificially intelligent cutting-edge technology -machine learning method was proposed to combine a variety of signals of non-contact sensors in several different positions. Higher accuracy was achieved, while maintaining the comfort brought by a non-contact sensor. The experimental results showed that the proposed strategy focused on the combination of clothing design technology and artificial intelligence technology. As a result, without changing the sensor materials, it enhances the comfort and precision of clothing, eliminates the comfort reduced by sensor close to the skin, and transforms inaccurate measurement into accurate measurement. </span></p>

Semantics-Based Document Categorization Employing Semi-Supervised Learning

2015 ◽  
pp. 112-140 ◽  
Author(s):  
Jan Žižka ◽  
František Dařena
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Real World ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Learning Method ◽  
Label Information ◽  
Document Categorization

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as “seeds” to automatically label the remaining volume of unlabeled items.

scholarly journals A note on label propagation for semi-supervised learning

2015 ◽  
Vol 7 (1) ◽  
pp. 18-30
Author(s):  
Zalán Bodó ◽  
Lehel Csató
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Label Propagation ◽  
Learning Method ◽  
The Past ◽  
Data Graph

Abstract Semi-supervised learning has become an important and thoroughly studied subdomain of machine learning in the past few years, because gathering large unlabeled data is almost costless, and the costly human labeling process can be minimized by semi-supervision. Label propagation is a transductive semi-supervised learning method that operates on the—most of the time undirected—data graph. It was introduced in [8] and since many variants were proposed. However, the base algorithm has two variants: the first variant presented in [8] and its slightly modified version used afterwards, e.g. in [7]. This paper presents and compares the two algorithms—both theoretically and experimentally—and also tries to make a recommendation which variant to use.

scholarly journals Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2981
Author(s):  
Haotai Sun ◽  
Xiaodong Zhu ◽  
Yuanning Liu ◽  
Wentao Liu
Keyword(s):  
Machine Learning ◽  
Radio Frequency ◽  
Indoor Navigation ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Dual Frequency ◽  
Evaluation Algorithm ◽  
Precision And Accuracy ◽  
Navigation Service

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.

scholarly journals Online Active Learning of Reject Option Classifiers

2020 ◽  
Vol 34 (04) ◽  
pp. 5652-5659
Author(s):  
Kulin Shah ◽  
Naresh Manwani
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Experimental Results ◽  
Reject Option ◽  
Novel Algorithms ◽  
Ramp Loss Function

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.

scholarly journals Deep Learning with Multiband Synthesis from Landsat-8 Satellite Imagery using Machine Learning

2019 ◽  
Vol 8 (6S2) ◽  
pp. 163-165
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Information Extraction ◽  
Satellite Imagery ◽  
Landsat 8 ◽  
Learning Method ◽  
Multi Scale ◽  
Viable System ◽  
Bear Witness

This examination article proposes a novel profound learning portrayal and division approach for moderate goals remote detecting picture investigation. An information extraction approach utilizing profound various leveled understanding for remote detecting picture is embraced as a proving ground for further increment in spatial goals symbolism. The thought is the way that we can receive a speedy filtering picture division in a profound learning highlight portrayal structure utilizing a profound learning method to deliver sensible measured bunches in portioned locales until it frames a super-object. Our commitment is to actualize a viable system for multi-scale picture investigation to address the issue of estimating vulnerability by and by. We at that point propose to test our strategy on two high goals remote detecting picture datasets that will yield brings about the type of multi-layered scenes that bear witness to the proficiency and unwavering quality of our proposed framework.

Development Direction of Machine Learning in the Era of Big Data

2014 ◽  
Vol 971-973 ◽  
pp. 1590-1593 ◽  
Author(s):  
Chun Yan Xue
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Supervised Learning ◽  
Data Structures ◽  
Learning Method ◽  
The Core ◽  
Device Integration ◽  
Development Direction ◽  
Huge Variety ◽  
Big Data Technology

The core objective of Big Data technology is trying to dig out valuable information from massing huge variety of data structures. In order to achieve these goals, Big Data technology must be combined with machine learning. The uniqueness of Big Data has also brought unprecedented challenges to machine learning, in order to cope with these challenges machine learning should focus on the development of semi-supervised learning method, integrated learning with device integration and transfer learning method.

Appraisal of Two Arabic Opinion Summarization Methods: Statistical Versus Machine Learning

2020 ◽  
Author(s):  
Imen Touati ◽  
Mariem Ellouze ◽  
Marwa Graja ◽  
Lamia Hadrich Belguith
Keyword(s):  
Machine Learning ◽  
Arabic Language ◽  
Experimental Results ◽  
Main Topic ◽  
News Article ◽  
Machine Learning Method ◽  
Learning Method ◽  
Opinion Summarization ◽  
Sentence Extraction ◽  
Many Sources

Abstract In this paper, we propose to overcome the challenge of digesting opinions in a news article. Our objective is to provide a summary of opinions delivered by many sources about a main topic in an Arabic news article. In literature, several studies addressed issues related to opinion summarization. However, we noticed a lack of studies that address this problem in Arabic language. So, we have proposed two different methods: multi-criteria and machine learning-based methods. We proceed by comparing the results provided by the proposed methods for opinionated sentence extraction. The proposed methods were evaluated using two feature types: text-based features and opinion-specific features. Experimental results show the robustness of machine learning method to extract opinionated sentences with consideration of two sets of features.

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