Topic
Class (philosophy)
About: Class (philosophy) is a research topic. Over the lifetime, 821 publications have been published within this topic receiving 28000 citations.
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TL;DR: In this paper , an attribute noise correction method was proposed to increase the performance of the classification algorithms used later, where the identification of noisy data was based on an error score assigned to each one of the attribute values in the dataset.
10 citations
01 Dec 2010
TL;DR: Boolean logic interpretations, as well as multiple-valued logic extensions, have been recently proposed for analogical proportions, which may provide a basis for proposing a plausible classification for an object d described in terms of a set of features, on the basis of three other already classified objects described on the same features.
Abstract: Boolean logic interpretations, as well as multiple-valued logic extensions, have been recently proposed for analogical proportions (ie statements of the form “a is to b as c is to d”), and for two other related formal proportions named reverse analogy (“what a is to b is the reverse of what c is to d”), and paralogy (“what a and b have in common c and d have it also”) These proportions relate items a, b, c, and d on the basis of their differences, or of their similarities This may provide a basis for proposing a plausible classification for an object d described in terms of a set of features, on the basis of three other already classified objects described on the same features, considering that if some proportion holds for a sufficiently large number of features, it may hold on the allocation of the classes as well This is the basis of a classification method which is tested on machine learning benchmarks for binary or multiple class problems with objects that have numerical features
10 citations
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TL;DR: In this paper, the authors propose a self-supervised framework with a contrastive objective that jointly learns to classify and localise objects without using any supervision, and then use these selfsupervised labels and boxes to train an image-based object detector.
Abstract: We tackle the problem of learning object detectors without supervision. Differently from weakly-supervised object detection, we do not assume image-level class labels. Instead, we extract a supervisory signal from audio-visual data, using the audio component to "teach" the object detector. While this problem is related to sound source localisation, it is considerably harder because the detector must classify the objects by type, enumerate each instance of the object, and do so even when the object is silent. We tackle this problem by first designing a self-supervised framework with a contrastive objective that jointly learns to classify and localise objects. Then, without using any supervision, we simply use these self-supervised labels and boxes to train an image-based object detector. With this, we outperform previous unsupervised and weakly-supervised detectors for the task of object detection and sound source localization. We also show that we can align this detector to ground-truth classes with as little as one label per pseudo-class, and show how our method can learn to detect generic objects that go beyond instruments, such as airplanes and cats.
10 citations
TL;DR: In this article, the authors evaluate three interventions that aim to improve learning outcomes in mathematics: teacher-led classes, CAL classes monitored by a technical supervisor, and CAL classes instructed by a teacher.
Abstract: This study provides novel evidence on the relative effectiveness of computer-assisted learning (CAL) software and traditional teaching. Based on a randomized controlled trial in Salvadoran primary schools, we evaluate three interventions that aim to improve learning outcomes in mathematics: (i) teacher-led classes, (ii) CAL classes monitored by a technical supervisor, and (iii) CAL classes instructed by a teacher. As all three interventions involve the same amount of additional mathematics lessons, we can directly compare the productivity of the three teaching methods. CAL lessons lead to larger improvements in students' mathematics skills than traditional teacher-centered classes. In addition, teachers add little to the e ectiveness of learning software. Overall, our results highlight the value of CAL approaches in an environment with poorly qualified teachers.
10 citations
TL;DR: In this article, the Fibonacci collocation method is used for approximately solving a class of nonlinear Pantograph differential equations with initial conditions, where the problem is firstly reduced into a nonlinear algebraic system via collocation points, and then the unknown coefficients of the approximate solution function are calculated.
Abstract: In this paper, Fibonacci collocation method is firstly used for approximately solving a class of systems of nonlinear Pantograph differential equations with initial conditions. The problem is firstly reduced into a nonlinear algebraic system via collocation points, later the unknown coefficients of the approximate solution function are calculated. Also, some problems are presented to test the performance of the proposed method by using the absolute error functions. Additionally, the obtained numerical results are compared with exact solutions of the test problems and approximate ones obtained with other methods in the literature.
9 citations