209,082 research outputs found
A Comprehensive Empirical Evaluation on Online Continual Learning
Online continual learning aims to get closer to a live learning experience by
learning directly on a stream of data with temporally shifting distribution and
by storing a minimum amount of data from that stream. In this empirical
evaluation, we evaluate various methods from the literature that tackle online
continual learning. More specifically, we focus on the class-incremental
setting in the context of image classification, where the learner must learn
new classes incrementally from a stream of data. We compare these methods on
the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average
accuracy, forgetting, stability, and quality of the representations, to
evaluate various aspects of the algorithm at the end but also during the whole
training period. We find that most methods suffer from stability and
underfitting issues. However, the learned representations are comparable to
i.i.d. training under the same computational budget. No clear winner emerges
from the results and basic experience replay, when properly tuned and
implemented, is a very strong baseline. We release our modular and extensible
codebase at https://github.com/AlbinSou/ocl_survey based on the avalanche
framework to reproduce our results and encourage future research.Comment: ICCV Visual Continual Learning Workshop 2023 accepted pape
Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient
Abundance of movie data across the internet makes it an obvious candidate for
machine learning and knowledge discovery. But most researches are directed
towards bi-polar classification of movie or generation of a movie
recommendation system based on reviews given by viewers on various internet
sites. Classification of movie popularity based solely on attributes of a movie
i.e. actor, actress, director rating, language, country and budget etc. has
been less highlighted due to large number of attributes that are associated
with each movie and their differences in dimensions. In this paper, we propose
classification scheme of pre-release movie popularity based on inherent
attributes using C4.5 and PART classifier algorithm and define the relation
between attributes of post release movies using correlation coefficient.Comment: 6 page
Implementation of Naive Bayes Classifier Algorithm to Evaluation in Utilizing Online Hotel Tax Reporting Application
The current implementation of tax reporting regional Pasuruan hotels have used online (Web-based), with the aim of reporting systems can run effectively and efficiently in receiving the financial statements especially from taxpayer property. Pasuruan as one small town quite rapidly in East Java, have implemented role models online tax filing system starting in 2015, with the amount of 6 hotels, there are several classes of hotels ranging from the budget class up to class three stars. After the application of the system running for 18 months (2015-2016), from existing data, conducted research on the analysis of the level of compliance of taxpayers reporting incomes in a hotel. On the research was designed and built a system to evaluate the level of compliance with the performance from the taxpayer (WP) in the 2nd year (2016) and are classified in categories (1) the taxpayer (WP) very obedient (ST), (2) the taxpayer (WP) is quite obedient (CT), (3) Taxpayers (WP) less obedient (KT). Input data will be processed using the technique of data mining algorithms Naive Bayes Classifier (NBC) to form the table of probability as a basis for the process of classification levels of taxpayer compliance. Based on the results of the measurement, the test results show with an accuracy of 50% i.e. 3 taxpayers is the very obedient (ST) to pay taxes. Then from the classification, the study could be made of recommendation solutions to guide the taxpayer in reporting revenues well and true
An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs
Kernel methods are considered an effective technique for on-line learning.
Many approaches have been developed for compactly representing the dual
solution of a kernel method when the problem imposes memory constraints.
However, in literature no work is specifically tailored to streams of graphs.
Motivated by the fact that the size of the feature space representation of many
state-of-the-art graph kernels is relatively small and thus it is explicitly
computable, we study whether executing kernel algorithms in the feature space
can be more effective than the classical dual approach. We study three
different algorithms and various strategies for managing the budget. Efficiency
and efficacy of the proposed approaches are experimentally assessed on
relatively large graph streams exhibiting concept drift. It turns out that,
when strict memory budget constraints have to be enforced, working in feature
space, given the current state of the art on graph kernels, is more than a
viable alternative to dual approaches, both in terms of speed and
classification performance.Comment: Author's version of the manuscript, to appear in Neurocomputing
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