Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

15.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Force Classification01:22

Force Classification

2.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.6K
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Upsampling01:22

Upsampling

678
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
678
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Permeation of particle through a four-helix-bundle model channel.

The Journal of chemical physics·2005
Same author

Immunogenicity, safety, and protective efficacy of an inactivated SARS-associated coronavirus vaccine in rhesus monkeys.

Vaccine·2005
Same author

Phase III study of the Eastern Cooperative Oncology Group (ECOG 2597): induction chemotherapy followed by either standard thoracic radiotherapy or hyperfractionated accelerated radiotherapy for patients with unresectable stage IIIA and B non-small-cell lung cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2005
Same author

Towards automatic clustering of protein sequences.

Proceedings. IEEE Computer Society Bioinformatics Conference·2005
Same author

Accelerating approximate subsequence search on large protein sequence databases.

Proceedings. IEEE Computer Society Bioinformatics Conference·2005
Same author

Novel mutation (V505D) of the TGFBI gene found in a Chinese family with lattice corneal dystrophy, type I.

Japanese journal of ophthalmology·2005

Related Experiment Videos

Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition.

Xuesong Zhang, Yan Zhuang, Wei Wang

    IEEE Transactions on Cybernetics
    |December 28, 2016
    PubMed
    Summary

    This study introduces a novel method combining synthetic data generation and transfer learning to improve object recognition with imbalanced datasets. The weighted synthetic minorities over-sampling technique (WSMOTE) enhances classifier accuracy for minority classes.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Object recognition faces challenges with small, imbalanced datasets, leading to overfitting and poor minority class prediction.
    • Class imbalance is a common issue in real-world machine learning applications, hindering classifier performance.

    Purpose of the Study:

    • To address class imbalanced object recognition by integrating synthetic data generation with transfer boosting.
    • To develop a robust classifier capable of handling skewed data distributions effectively.

    Main Methods:

    • A novel weighted synthetic minorities over-sampling technique (WSMOTE) was proposed to generate weighted synthetic instances.
    • Developed a class imbalanced transfer boosting algorithm (WSMOTE-TrAdaboost) within the transfer Adaboost framework.
    • Utilized Bag-of-Words with SURF features and Histogram of Oriented Gradient features for image representation.

    Main Results:

    • WSMOTE-TrAdaboost demonstrated effectiveness on four diverse datasets (Office, Caltech256, SUN2012, VOC2012).
    • The proposed method showed improved prediction accuracy on minority classes compared to baseline algorithms.
    • Experimental results confirmed the robustness of the approach for object recognition tasks.

    Conclusions:

    • The proposed WSMOTE-TrAdaboost algorithm effectively mitigates the problem of class imbalanced object recognition.
    • Combining SMOTE with transfer boosting offers a promising direction for robust classifier training on skewed data.
    • The approach provides a valuable contribution to improving the performance of object recognition systems.