scholarly journals Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds

2021 ◽  
Author(s):  
Xiaocheng Li ◽  
Yinyu Ye
Keyword(s):  
Linear Programming ◽  
Learning Algorithm ◽  
Equivalent Form ◽  
Sample Average Approximation ◽  
Smoothness Condition ◽  
Regularity Conditions ◽  
Linear Programs ◽  
Constraint Matrix ◽  
The Past ◽  
Dependent Learning

We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are independently and identically drawn from an unknown distribution and revealed sequentially over time. Virtually all existing online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LPs), and their analyses were focused on the aggregate objective value and solving the packing LP, where all coefficients in the constraint matrix and objective are nonnegative. However, two major open questions were as follows. (i) Does the set of LP optimal dual prices learned in the existing algorithms converge to those of the “offline” LP? (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative? We resolve these two questions by establishing convergence results for the dual prices under moderate regularity conditions for general LP problems. Specifically, we identify an equivalent form of the dual problem that relates the dual LP with a sample average approximation to a stochastic program. Furthermore, we propose a new type of OLP algorithm, action-history-dependent learning algorithm, which improves the previous algorithm performances by taking into account the past input data and the past decisions/actions. We derive an [Formula: see text] regret bound (under a locally strong convexity and smoothness condition) for the proposed algorithm, against the [Formula: see text] bound for typical dual-price learning algorithms, where n is the number of decision variables. Numerical experiments demonstrate the effectiveness of the proposed algorithm and the action-history-dependent design.

scholarly journals A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs

2021 ◽  
Vol 2021 ◽  
pp. 1-4
Author(s):  
Chengxue Zhang ◽  
Debin Kong ◽  
Peng Pan ◽  
Mingyuan Zhou
Keyword(s):  
Linear Programming ◽  
Random Matrix ◽  
Optimal Solution ◽  
Full Rank ◽  
Linear Programs ◽  
Program Problem ◽  
Constraint Matrix ◽  
Right Hand ◽  
Linear Program Problem ◽  
The Matrix

In a linear programming for horizontally partitioned data, the equality constraint matrix is divided into groups of rows. Each group of the matrix rows and the corresponding right-hand side vector are owned by different entities, and these entities are reluctant to disclose their own groups of rows or right-hand side vectors. To calculate the optimal solution for the linear programming in this case, Mangasarian used a random matrix of full rank with probability 1, but an event with probability 1 is not a certain event, so a random matrix of full rank with probability 1 does not certainly happen. In this way, the solution of the original linear programming is not equal to the solution of the secure linear programming. We used an invertible random matrix for this shortcoming. The invertible random matrix converted the original linear programming problem to a secure linear program problem. This secure linear programming will not reveal any of the privately held data.

scholarly journals Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 300
Author(s):  
Mark Lokanan ◽  
Susan Liu
Keyword(s):  
Machine Learning ◽  
Financial Literacy ◽  
Learning Algorithm ◽  
Demographic Characteristics ◽  
Financial Knowledge ◽  
The Past ◽  
Machine Learning Model ◽  
Long Time ◽  
Regulatory Organization ◽  
Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

scholarly journals Novel AI driven approach to classify infant motor functions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Reich ◽  
Dajie Zhang ◽  
Tomas Kulvicius ◽  
Sven Bölte ◽  
Karin Nielsen-Saines ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Movement Pattern ◽  
Video Stream ◽  
Typically Developing ◽  
The Past ◽  
Movement Assessment ◽  
Computer Based ◽  
Ablation Study ◽  
First Time ◽  
Efficient Screening

AbstractThe past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network’s architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

scholarly journals ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

2017 ◽  
Vol 10 (13) ◽  
pp. 284
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Online Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Past ◽  
Slow Development ◽  
Online Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency.  In this study we present a novel online learning algorithm for network based computational operations in image processing setting

A management advisory system using computerized optimization techniques

1970 ◽  
Vol 6 (4) ◽  
pp. 143-147 ◽  
Author(s):  
E. M. L. Beale
Keyword(s):  
Linear Programming ◽  
Computer Programs ◽  
Optimization Techniques ◽  
Advisory Services ◽  
Farm Planning ◽  
The Past ◽  
Advisory System ◽  
Planning Problems ◽  
Management Advisory

During the past twenty years, linear programming has been developed as a tool for planning enterprises in government and industry using a computer. It has also been used to supplement farm advisory services based on traditional accountancy methods. More recently, computer programs have been developed for preparing the equations representing the problem. The author, as consultant to the ‘MASCOT’ project, has contributed greatly to the development of a complete working system that accepts and analyses farm planning problems in agricultural terms.

scholarly journals An Effective Integration of Domain Knowledge in-to Deep Neural Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 507-509
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Ease Of Use ◽  
The Past ◽  
Complex Models ◽  
Effective Integration

Machine learning in recent years has become an integral part of our day to day life and the ease of use has improved a lot in the past decade.There are various ways to make the model to work in smaller devices.A modest method to advance any machine learning algorithm to work in smaller devices is to provide the output of large complex models as input to smaller models which can be easily deployed into mobile phones .We provided a framework where the large models can even learn the domain knowledge which is integrated as first-order logic rules and explicitly includes that knowledge into the smaller model by simultaneously training of both the models.This can be achieved by transfer learning where the knowledge learned by one model can be used to teach the other model.Domain knowledge integration is the most critical part here and it can be done by using some of the constraint principles where the scope of the data is reduced based upon the constraints mentioned. One of the best representation of domain knowledge is logic rules where the knowledge is encoded as predicates.This framework provides a way to integrate human knowledge into deep neural networks that can be easily deployed into any devices.

scholarly journals Detection of Novel Corona Virus Using Machine Learning and Image Recognition

2020 ◽  
Vol 6 (12) ◽  
pp. 394-397
Author(s):  
Dhruv Garg and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recent Past ◽  
Great Precision ◽  
The Past ◽  
Corona Virus ◽  
The World ◽  
Affected Person ◽  
Precise Machine ◽  
Self Learning

In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne nature of this disease has made it highly contagious which has led to a great number of people being infected very fast. This requires a new method of testing that is faster and more precise. Machine Learning has allowed us to develop sophisticated self-learning models that can learn from data being fed and decide on entirely new options. In the past we have used different Machine Learning algorithm to make models on different biomedical dataset to detect various kind of acute or chronic diseases. Here we have developed a model that successfully detects severe cases of Novel corona virus affected person with great precision.

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