8 research outputs found
Unexpected Cost of Korean Wave during Pandemic Covid-19 in Makassar, South Sulawesi
This study discuss phenomenal success Korean Wave during pandemic covid-19 in Makassar, South Sulawesi, Indonesia. In recent years, Korean wave has become an addiction to young people in Makassar. This paper aims to explore the young people’s habit during pandemic covid-19, how they spend their leisure time throughout large scale social restriction (PSBB). Through the process of interviews, questionnaires, media, and article, this study result that the young people are willing to pay more to fulfill their hobby of watching Kdrama
On the relative value of weak information of supervision for learning generative models: An empirical study
Weakly supervised learning is aimed to learn predictive models from partially supervised data, an easy-to-collect alternative to the costly standard full supervision. During the last decade, the research community has striven to show that learning reliable models in specific weakly supervised problems is possible. We present an empirical study that analyzes the value of weak information of supervision throughout its entire spectrum, from none to full supervision. Its contribution is assessed under the realistic assumption that a small subset of fully supervised data is available. Particularized in the problem of learning with candidate sets, we adapt Cozman and Cohen [1] key study to learning from weakly supervised data. Standard learning techniques are used to infer generative models from this type of supervision with both synthetic and real data. Empirical results suggest that weakly labeled data is helpful in realistic scenarios, where fully labeled data is scarce, and its contribution is directly related to both the amount of information of supervision and how meaningful this information is
Applications of gravitational search algorithm in engineering
Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems
Weakly supervised learning via statistical sufficiency
The Thesis introduces a novel algorithmic framework for
weakly supervised learn- ing, namely, for any any problem in
between supervised and unsupervised learning, from the labels
standpoint. Weak supervision is the reality in many applications
of machine learning where training is performed with partially
missing, aggregated- level and/or noisy labels. The approach is
grounded on the concept of statistical suf- ficiency and its
transposition to loss functions. Our solution is problem-agnostic
yet constructive as it boils down to a simple two-steps
procedure. First, estimate a suffi- cient statistic for the
labels from weak supervision. Second, plug the estimate into a
(newly defined) linear-odd loss function and learn the model by
any gradient-based solver, with a simple adaptation. We apply the
same approach to several challeng- ing learning problems: (i)
learning from label proportions, (ii) learning with noisy labels
for both linear classifiers and deep neural networks, and (iii)
learning from feature-wise distributed datasets where the entity
matching function is unknown