Learning to Rank for Review Rating Prediction

2014 ◽  
Vol 556-562 ◽  
pp. 6286-6289
Author(s):  
Nian Li ◽  
Li Yin ◽  
Qing Xi Peng
Keyword(s):  
Learning To Rank ◽  
Empirical Studies ◽  
Real Life ◽  
Classification Problem ◽  
Machine Learning Method ◽  
Regression Problem ◽  
The Public ◽  
Rating Prediction ◽  
Multi Classification ◽  
New Machine

The Internet has experienced profound changes. Large amount of user-generated-contents provide valuable information to the public. Customers usually express their opinion in online shopping. After they finish the reviews, they give an overall rating to the product or service. In this paper, we focus on the review rating prediction problem. Previous studies usually regard this problem as a regression problem. We take a new machine learning method to solve the problem. Learning to rank method has been exploited to tackle the prediction. After feature selection, the maximum entropy classifier has been employed to solve the multi-classification problem. The real life dataset has been crawled to verify the proposed method. Empirical studies demonstrate the proposed method outperform the baseline methods.

A Multi-Classification Method Based on Gaussian Processes

2012 ◽  
Vol 198-199 ◽  
pp. 1333-1337 ◽  
Author(s):  
San Xi Wei ◽  
Zong Hai Sun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Good Accuracy ◽  
Classification Problem ◽  
Decision Time ◽  
Support Vector ◽  
Regression Problem ◽  
Multi Classification

Gaussian processes (GPs) is a very promising technology that has been applied both in the regression problem and the classification problem. In recent years, models based on Gaussian process priors have attracted much attention in the machine learning. Binary (or two-class, C=2) classification using Gaussian process is a very well-developed method. In this paper, a Multi-classification (C>2) method is illustrated, which is based on Binary GPs classification. A good accuracy can be obtained through this method. Meanwhile, a comparison about decision time and accuracy between this method and Support Vector Machine (SVM) is made during the experiments.

An Empirical Study of Ranking-Oriented Cross-Project Software Defect Prediction

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1511-1538 ◽  
Author(s):  
Guoan You ◽  
Feng Wang ◽  
Yutao Ma
Keyword(s):  
Binary Classification ◽  
Learning To Rank ◽  
Classification Problem ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Ranking Problem ◽  
Regression Problem ◽  
Software Defect ◽  
One And Many ◽  
Cross Project

Cross-project defect prediction (CPDP) has recently become very popular in the field of software defect prediction. It was generally treated as a binary classification problem or a regression problem in most of previous studies. However, these existing CPDP methods may be not suitable for those software projects that have limited manpower and budget. To address the issue of priority estimation for buggy software entities, in this paper CPDP is formulated as a ranking problem. Inspired by the idea of the pointwise approach to learning to rank, we propose a ranking-oriented CPDP approach called ROCPDP. A case study conducted on the datasets collected from AEEEM and PROMISE shows that ROCPDP outperforms the eight baseline methods in two CPDP scenarios, namely one-to-one and many-to-one. Besides, in the many-to-one scenario ROCPDP is, by and large, comparable to the best baseline method performed in a specific within-project defect prediction scenario.

Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks

2020 ◽  
Vol 11 (2) ◽  
pp. 1-17
Author(s):  
Shudong Liu ◽  
Ke Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Slot ◽  
Empirical Studies ◽  
Real Life ◽  
Periodic Trajectories ◽  
The Public ◽  
Friend Recommendation ◽  
Location Based Social Networks ◽  
Friend Recommendations

The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.

Regression and Classification Method Based on Gaussian Processes

2014 ◽  
Vol 971-973 ◽  
pp. 1949-1952
Author(s):  
Xing Hui Wu ◽  
Yu Ping Zhou
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Classification Problem ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Regression Problem ◽  
Development Direction

Gaussian processes is a kind of machine learning method developed in recent years and also a promising technology that has been applied both in the regression problem and the classification problem. In this paper, the general principle of regression and classification based on Gaussian process and experimental verification was described. A comparison about accuracy between this method and Support Vector Machine (SVM) is made during the experiments.Finally, it was summarized of the regression and classification of Gaussian process application and future development direction.

The Speech-Language Pathologist in the Public Schools: Preparation for Real Life Practices From the Perspective of a Speech-Language Pathology Program Coordinator

2010 ◽  
Vol 11 (2) ◽  
pp. 60-65
Author(s):  
Francine Wenhardt
Keyword(s):  
Public Schools ◽  
School Setting ◽  
Real Life ◽  
Effective School ◽  
Program Coordinator ◽  
Educational Preparation ◽  
The Public ◽  
Speech Language Pathologist ◽  
New Graduate ◽  
Language Pathology

Abstract The speech-language pathologist (SLP) working in the public schools has a wide variety of tasks. Educational preparation is not all that is needed to be an effective school-based SLP. As a SLP currently working in the capacity of a program coordinator, the author describes the skills required to fulfill the job requirements and responsibilities of the SLP in the school setting and advises the new graduate regarding the interview process and beginning a career in the public schools.

Auditors' and Investors' Perceptions of the “Expectation Gap”

2001 ◽  
Vol 15 (4) ◽  
pp. 345-358 ◽  
Author(s):  
John E. McEnroe ◽  
Stanley C. Martens
Keyword(s):  
Empirical Studies ◽  
Primary User ◽  
Financial Statement ◽  
User Group ◽  
Accounting Profession ◽  
The Public ◽  
Government Accounting ◽  
Expectation Gap ◽  
The Difference

The auditing “expectation gap” refers to the difference between (1) what the public and other financial statement users perceive auditors' responsibilities to be and (2) what auditors believe their responsibilities entail. The notion of this divergence receives much attention in the accounting literature (i.e., Commission on Auditors' Responsibilities 1978; Guy and Sullivan 1988; AICPA 1993; U.S. Government Accounting Office 1996). Although prior empirical studies encompass certain expectations associated with a range of audit services, these papers often involve the opinions of bankers as the primary user group employed in the research (Nair and Rittenberg 1987; Lowe and Pany 1995). In contrast, this study extends the prior research by directly comparing audit partners' and investors' perceptions of auditors' responsibilities involving various dimensions of the attest function. We conducted the study to determine if an expectation gap currently exists and we find that it does; investors have higher expectations for various facets and/or assurances of the audit than do auditors. Our findings serve as evidence that the accounting profession should engage in appropriate measures to reduce this expectation gap.

The Economics of Government

2020 ◽  
Author(s):  
Vito Tanzi
Keyword(s):  
Public Finance ◽  
Public Sector ◽  
Social Welfare ◽  
Real Life ◽  
Rent Seeking ◽  
Major Conclusion ◽  
The Public ◽  
The Past ◽  
Private Market

This book deals with practical or real life aspects of public finance. It focuses on the growth in the activities of governments, in a world that expects more than in the past from governments. The book focuses on the growing complexity in both the work of the private market and that of the public sector. It stresses that part of the growing complexity is due to the more ambitious role that governments tried to play today, while part is due to choices made by governments, so that complexity may be partly avoidable. This was important in the different pursuit of social welfare by different countries. Complexity has increased opportunities for abuses, for rent seeking, and for mistakes in policies. It may also have increased the attraction of populist policies that claim to offer magical or easy solutions to problems. A major conclusion of the book is that the objective of simplicity in laws and in policies should be given more importance by both economists and governments.

Psychology: Volume 1

2019 ◽  
Keyword(s):  
Positive Psychology ◽  
Human Life ◽  
Empirical Studies ◽  
Real Life ◽  
Human Beings ◽  
Cognitive Capability ◽  
Affective Processes ◽  
Abstract Level ◽  
The Impact ◽  
Cognition And Affect

This survey of research on psychology in five volumes is a part of a series undertaken by the ICSSR since 1969, which covers various disciplines under social science. Volume One of this survey, Cognitive and Affective Processes, discusses the developments in the study of cognitive and affective processes within the Indian context. It offers an up-to-date assessment of theoretical developments and empirical studies in the rapidly evolving fields of cognitive science, applied cognition, and positive psychology. It also analyses how pedagogy responds to a shift in the practices of knowing and learning. Additionally, drawing upon insights from related fields it proposes epithymetics–desire studies – as an upcoming field of research and the volume investigates the impact of evolving cognitive and affective processes in Indian research and real life contexts. The development of cognitive capability distinguishes human beings from other species and allows creation and use of complex verbal symbols, facilitates imagination and empowers to function at an abstract level. However, much of the vitality characterizing human life is owed to the diverse emotions and desires. This has made the study of cognition and affect as frontier areas of psychology. With this in view, this volume focuses on delineating cognitive scientific contributions, cognition in educational context, context, diverse applications of cognition, psychology of desire, and positive psychology. The five chapters comprising this volume have approached the scholarly developments in the fields of cognition and affect in innovative ways, and have addressed basic as well applied issues.

scholarly journals Changes in the Soundscape of the Public Space Close to a Highway by a Noise Control Intervention

2021 ◽  
Vol 13 (9) ◽  
pp. 5284
Author(s):  
Timothy Van Renterghem ◽  
Francesco Aletta ◽  
Dick Botteldooren
Keyword(s):  
Public Space ◽  
Real Life ◽  
Noise Abatement ◽  
Acoustic Environment ◽  
The Public ◽  
Control Intervention ◽  
Before And After ◽  
Noise Shielding ◽  
Visual Preferences ◽  
The Impact

The deployment of measures to mitigate sound during propagation outdoors is most often a compromise between the acoustic design, practical limitations, and visual preferences regarding the landscape. The current study of a raised berm next to a highway shows a number of common issues like the impact of the limited length of the noise shielding device, initially non-dominant sounds becoming noticeable, local drops in efficiency when the barrier is not fully continuous, and overall limited abatement efficiencies. Detailed assessments of both the objective and subjective effect of the intervention, both before and after the intervention was deployed, using the same methodology, showed that especially the more noise sensitive persons benefit from the noise abatement. Reducing the highest exposure levels did not result anymore in a different perception compared to more noise insensitive persons. People do react to spatial variation in exposure and abatement efficiency. Although level reductions might not be excessive in many real-life complex multi-source situations, they do improve the perception of the acoustic environment in the public space.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

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