Jove
Visualize
Contact Us

Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Signals01:30

Classification of Signals

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...
Aggregates Classification01:29

Aggregates Classification

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...
Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Force Classification01:22

Force Classification

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,...

You might also read

Related Articles

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

Sort by
Same author

SCALABLE FUSED LASSO SVM FOR CONNECTOME-BASED DISEASE PREDICTION.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2015
Same author

Tuning support vector machines for minimax and Neyman-Pearson classification.

IEEE transactions on pattern analysis and machine intelligence·2010
See all related articles
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 Experiment Videos

L2 kernel classification.

JooSeuk Kim1, Clayton D Scott

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109-2122, USA. stannum@umich.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 21, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel L2 kernel classifier for statistical learning, optimizing the integrated squared error of density differences. The method offers statistical guarantees and performs competitively with Support Vector Machines in high dimensions.

Related Experiment Videos

Area of Science:

  • Statistical Learning
  • Machine Learning
  • Nonparametric Statistics

Background:

  • Nonparametric kernel methods like Kernel Density Estimation (KDE) and Support Vector Machines (SVM) are established in statistical learning.
  • Existing methods often focus on direct classification or density estimation tasks.

Purpose of the Study:

  • To propose a novel kernel classifier optimizing the L2 or integrated squared error (ISE) of a "difference of densities."
  • To provide theoretical performance guarantees and practical extensions for high-dimensional data.

Main Methods:

  • Developed a kernel classifier based on optimizing the L2/ISE of density differences, utilizing the Gaussian kernel.
  • The classifier is sparse, derived from solving a quadratic program, similar to SVM.
  • Introduced a regularization parameter to enhance performance in high-dimensional settings (>15 dimensions).

Main Results:

  • Established statistical performance guarantees, including finite sample oracle inequalities and strong consistency (ISE and probability of error).
  • The basic L2 kernel classifier shows limitations in dimensions greater than 15.
  • The regularized version demonstrates competitive performance against SVM in high dimensions.

Conclusions:

  • The proposed L2 kernel classifier is a theoretically sound and practically viable alternative for classification tasks.
  • The regularization extension makes the method effective for high-dimensional data.
  • The approach offers a new perspective on kernel-based learning by focusing on density differences.