scholarly journals No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously

2017 ◽  
Author(s):  
Xiaojin Zhu ◽  
Ji Liu ◽  
Manuel Lopes
Keyword(s):  
Linear Regression ◽  
Real World ◽  
Theoretical Study ◽  
Classroom Setting ◽  
Optimal Partition ◽  
Training Set ◽  
Large Classes ◽  
Left Behind ◽  
Personalized Education ◽  
Teaching Dimension

We present a theoretical study of algorithmic teaching in the setting where the teacher must use the same training set to teach multiple learners. This problem is a theoretical abstraction of the real-world classroom setting in which the teacher delivers the same lecture to academically diverse students. We define a minimax teaching criterion to guarantee the performance of the worst learner in the class. We prove that the teaching dimension increases with class diversity in general. For the classes of conjugate Bayesian learners and linear regression learners, respectively, we exhibit corresponding minimax teaching set. We then propose a method to enhance teaching by partitioning the class into sections. We present cases where the optimal partition minimizes overall teaching dimension while maintaining the guarantee on all learners. Interestingly, we show personalized education (one learner per section) is not necessarily the optimal partition. Our results generalize algorithmic teaching to multiple learners and offer insight on how to teach large classes.

Educating Heroes

Worldview ◽  
1983 ◽  
Vol 26 (4) ◽  
pp. 10-13
Author(s):  
John E. Becker
Keyword(s):  
Real World ◽  
Writing Programs ◽  
Student Government ◽  
Business Writing ◽  
The Real ◽  
English Departments ◽  
Left Behind

“The real world.” How our students love the phrase! An ex-linguist of my acquaintance, bitter from years of mistreatment in English departments, has come to rest at last behind a very large oak desk in a generously appointed office at a large university. She is coordinator of business-writing programs, and a sense of authority informs her words now as she talks of “those of us who work in the real world.” Meanwhile the benighted rest of us, left behind on university faculties, complacently accept the givenness of that extrauniversity “real world.” At graduation rituals we sit smiling under our tassels and hear each speaker, from the head of student government to the chancellor, from professor to famous guest, tell our students that they are about to enter the “real world.”

scholarly journals Sampling the Porridge: A Comparison of Ordered Variable Regression with F and R2 and Multiple Linear Regression with Corrected F and R2 in the Presence of Multicollinearity

2020 ◽  
Vol 18 (1) ◽  
pp. 2-39
Author(s):  
Grayson L. Baird ◽  
Stephen L. Bieber
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Real World ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Real World Data ◽  
World Data

Differences between the multiple linear regression model with Corrected R2 and Corrected F and the ordered variable regression model with R2 and F when intercorrelation is present are illustrated with simulated and real-world data.

Validation-Based Sparse Gaussian Process Classifier Design

2009 ◽  
Vol 21 (7) ◽  
pp. 2082-2103 ◽  
Author(s):  
Shirish Shevade ◽  
S. Sundararajan
Keyword(s):  
Bayesian Methods ◽  
Real World ◽  
Basis Vector ◽  
Data Sets ◽  
Training Set ◽  
Classifier Design ◽  
Set Size ◽  
Benchmark Data ◽  
Regression Problems ◽  
Classification And Regression

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better or comparable generalization performance over existing methods.

scholarly journals Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data

2020 ◽  
Vol 34 (04) ◽  
pp. 6853-6860
Author(s):  
Xuchao Zhang ◽  
Xian Wu ◽  
Fanglan Chen ◽  
Liang Zhao ◽  
Chang-Tien Lu
Keyword(s):  
Real World ◽  
Large Scale ◽  
Learning Algorithm ◽  
Noisy Data ◽  
Training Set ◽  
Robust Learning ◽  
Robust Model ◽  
Small Set ◽  
Real World Datasets ◽  
Theoretical Analyses

The success of training accurate models strongly depends on the availability of a sufficient collection of precisely labeled data. However, real-world datasets contain erroneously labeled data samples that substantially hinder the performance of machine learning models. Meanwhile, well-labeled data is usually expensive to obtain and only a limited amount is available for training. In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. To leverage the information contained via the clean labels, we propose a novel self-paced robust learning algorithm (SPRL) that trains the model in a process from more reliable (clean) data instances to less reliable (noisy) ones under the supervision of well-labeled data. The self-paced learning process hedges the risk of selecting corrupted data into the training set. Moreover, theoretical analyses on the convergence of the proposed algorithm are provided under mild assumptions. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed approach can achieve a considerable improvement in effectiveness and robustness to existing methods.

scholarly journals Vaksin Strategy as Enjoyment Chemical Approach for Senior High School Students

2018 ◽  
Vol 2 (2) ◽  
pp. 125
Author(s):  
Layta Dinira
Keyword(s):  
Waste Water ◽  
High School ◽  
High School Students ◽  
Real World ◽  
Theoretical Study ◽  
Positive Impact ◽  
School Students ◽  
Science Camp ◽  
Learning Interest ◽  
Senior High School Students

<p>Desire to apply knowledge gained in school is the characteristic of high school students. These characteristic actually has been accommodated into 2013 curriculum. However, low interest of high school students to study chemistry was still found. Various learning methods have been developed to improve learning interest in the classroom. The effort to increase students' interest in chemistry can also be done outside the classroom. In this paper will be presented a theoretical study of joyful learning real world chemistry through VAKSIN strategy during school break. The strategy will be given in two ways, through science camps or excursion. Materials to be provided during the science camp are making green chemistry paint, exploration of cat litters, and simulations of waste water purification. Excursion can go into two places, the industry or university. VAKSIN strategy will have positive impact on students, teachers, industries, and universities.</p><p><em> </em></p><p><strong>Keywords</strong>: high school students, joyful learning, real world chemistry, VAKSIN strategy</p>

scholarly journals The Leukocyte Subtype Counts and Ratios Can Effectively Predict the Risk of Arterial Stiffness Assessed by Cardio-Ankle Vascular Index: A Retrospective Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Yaoling Wang ◽  
Ruiyun Wang ◽  
Lijuan Bai ◽  
Yun Liu ◽  
Lihua Liu ◽  
...  
Keyword(s):  
Risk Factor ◽  
Linear Regression ◽  
Arterial Stiffness ◽  
Decision Models ◽  
Receiver Operating Curve ◽  
Simple Linear Regression ◽  
Training Set ◽  
Potential Indicator ◽  
High Risk Factor ◽  
Validation Set

Background: Arterial stiffness was the pathological basis and risk factor of cardiovascular diseases, with chronic inflammation as the core characteristic. We aimed to analyze the association between the arterial stiffness measured by cardio-ankle vascular index (CAVI) and indicators reflecting the inflammation degree, such as count of leukocyte subtypes, platelet, and monocyte-to-lymphocyte ratio (MLR), etc.Methods: The data of inpatients from November 2018 to November 2019 and from December 2019 to September 2020 were continuously collected as the training set (1,089 cases) and the validation set (700 cases), respectively. A retrospective analysis of gender subgroups was performed in the training set. The association between inflammatory indicators and CAVI or arterial stiffness by simple linear regression, multiple linear regression, and logistic regression was analyzed. The effectiveness of the inflammation indicators and the CAVI decision models to identify arterial stiffness by receiver operating curve (ROC) in the training and validation set was evaluated.Results: The effect weights of MLR affecting the CAVI were 12.87% in men. MLR was the highest risk factor for arterial stiffness, with the odds ratio (95% confidence interval) of 8.95 (5.04–184.79) in men after adjusting the covariates. A cutpoint MLR of 0.19 had 70% accuracy for identifying arterial stiffness in all participants. The areas under the ROC curve of the CAVI decision models for arterial stiffness were &gt;0.80 in the training set and validation set.Conclusions: The MLR might be a high-risk factor for arterial stiffness and could be considered as a potential indicator to predict arterial stiffness.

scholarly journals Effect of Ribavirin on Recovery Time in COVID-19 Patients: A Real-world Retrospective Cohort Study

2020 ◽  
Author(s):  
Yuanlong Hu ◽  
Xue Zhu ◽  
Ning Shen ◽  
Xinhua Jia ◽  
Xingcai Zhang ◽  
...  
Keyword(s):  
Cohort Study ◽  
Linear Regression ◽  
Regression Model ◽  
Real World ◽  
Linear Regression Model ◽  
Retrospective Cohort Study ◽  
Recovery Time ◽  
Retrospective Cohort ◽  
Specific Drug ◽  
Clinical Benefits

Abstract Background: Up to now, there is still no specific drug against COVID-19. However, Ribavirin may bring clinical benefits to COVID-19 patients.Methods: This study was designed as a real-world retrospective cohort study based on electronic medical record (EMR), and linear regression model was used to evaluate the effect of Ribavirin on recovery time.Results: 342 patients were enrolled in this study. Both unadjusted and unadjusted models showed that interferon or Lopinavir-Ritonavir combined with Ribavirin could shorten the recovery time of patients, which was evident in all subgroups considered except the severe subgroup and after fine adjustments.Conclusion: This study shows that interferon or Lopinavir-Ritonavir combined with Ribavirin can shorten the recovery time of patients with non-severe COVID-19.

scholarly journals On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 820 ◽  
Author(s):  
Ji-jun ◽  
Mahmoudi ◽  
Baleanu ◽  
Maleki
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Real World ◽  
Regression Models ◽  
Computational Approach ◽  
Parametric Methods ◽  
Linear Regression Models ◽  
Science Data ◽  
Non Linear ◽  
Real World Problems

In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.

Introduction

2017 ◽  
Author(s):  
Stephen John Hartnett ◽  
Eleanor Novek ◽  
Jennifer K. Wood
Keyword(s):  
Violent Crime ◽  
Real World ◽  
Prison Education ◽  
Cycle Of Violence ◽  
Industrial Complex ◽  
Prison Industrial Complex ◽  
Men And Women ◽  
Left Behind ◽  
Research And Education

This introductory chapter discusses how the Prison Communication, Activism, Research, and Education Collective (PCARE) attempts to put democracy into practice by merging prison education and activism. While dozens of studies have described what is wrong with America's prison-industrial complex—its embedded racism and sexism, its perpetual violence, its skewed judicial and legislative aspects, and its corresponding media spectacles, among others—the chapter presents real-world answers based on years of pragmatic activism and engaged teaching. It recognizes that the men and women in prisons and jails have left behind them trails of wreckage—they harmed others and caused immeasurable pain. Meanwhile, the victims of violent crime attest that their lives are forever altered. The chapter foregrounds these facts and argues that the only way to end the cycle of violence is by moving past the anger and fear.

Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure

2002 ◽  
Vol 14 (1) ◽  
pp. 21-41 ◽  
Author(s):  
Marco Saerens ◽  
Patrice Latinne ◽  
Christine Decaestecker
Keyword(s):  
Real World ◽  
Classification Accuracy ◽  
A Priori ◽  
Training Set ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
The Real ◽  
Trained Classifier ◽  
A Priori Probability

It sometimes happens (for instance in case control studies) that a classifier is trained on a data set that does not reflect the true a priori probabilities of the target classes on real-world data. This may have a negative effect on the classification accuracy obtained on the real-world data set, especially when the classifier's decisions are based on the a posteriori probabilities of class membership. Indeed, in this case, the trained classifier provides estimates of the a posteriori probabilities that are not valid for this real-world data set (they rely on the a priori probabilities of the training set). Applying the classifier as is (without correcting its outputs with respect to these new conditions) on this new data set may thus be suboptimal. In this note, we present a simple iterative procedure for adjusting the outputs of the trained classifier with respect to these new a priori probabilities without having to refit the model, even when these probabilities are not known in advance. As a by-product, estimates of the new a priori probabilities are also obtained. This iterative algorithm is a straightforward instance of the expectation-maximization (EM) algorithm and is shown to maximize the likelihood of the new data. Thereafter, we discuss a statistical test that can be applied to decide if the a priori class probabilities have changed from the training set to the real-world data. The procedure is illustrated on different classification problems involving a multilayer neural network, and comparisons with a standard procedure for a priori probability estimation are provided. Our original method, based on the EM algorithm, is shown to be superior to the standard one for a priori probability estimation. Experimental results also indicate that the classifier with adjusted outputs always performs better than the original one in terms of classification accuracy, when the a priori probability conditions differ from the training set to the real-world data. The gain in classification accuracy can be significant.

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