Example of Neural Processing Letters format
Recent searches

Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
Look Inside
Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format Example of Neural Processing Letters format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Neural Processing Letters — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Computer Networks and Communications #102 of 334 up up by 5 ranks
Artificial Intelligence #91 of 227 down down by 8 ranks
Software #159 of 389 up up by 23 ranks
Neuroscience (all) #61 of 110 up up by 5 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 814 Published Papers | 3451 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 26/06/2020
Related journals
Insights
General info
Top papers
Popular templates
Get started guide
Why choose from SciSpace
FAQ

Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 19.8
SJR: 2.882
SNIP: 3.86
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 6.2
SJR: 0.427
SNIP: 1.319
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 6.9
SJR: 0.638
SNIP: 1.44
open access Open Access

Springer

Quality:  
Good
CiteRatio: 4.6
SJR: 0.983
SNIP: 1.376

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.891

12% from 2018

Impact factor for Neural Processing Letters from 2016 - 2019
Year Value
2019 2.891
2018 2.591
2017 1.787
2016 1.62
graph view Graph view
table view Table view

4.2

14% from 2019

CiteRatio for Neural Processing Letters from 2016 - 2020
Year Value
2020 4.2
2019 3.7
2018 3.5
2017 2.7
2016 2.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 12% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 14% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.463

21% from 2019

SJR for Neural Processing Letters from 2016 - 2020
Year Value
2020 0.463
2019 0.589
2018 0.569
2017 0.51
2016 0.399
graph view Graph view
table view Table view

0.887

22% from 2019

SNIP for Neural Processing Letters from 2016 - 2020
Year Value
2020 0.887
2019 1.134
2018 1.015
2017 0.872
2016 0.777
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 21% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 22% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Neural Processing Letters

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

Springer

Neural Processing Letters

Neural Processing Letters is an international journal publishing research results and innovative ideas in all fields of artificial neural networks. Prospective authors are encouraged to submit letters concerning any aspect of the Artificial Neural Networks field including, but...... Read More

Computer Networks and Communications

Artificial Intelligence

Software

General Neuroscience

Computer Science

i
Last updated on
26 Jun 2020
i
ISSN
1370-4621
i
Impact Factor
High - 1.521
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1023/A:1018628609742
Least Squares Support Vector Machine Classifiers
Johan A. K. Suykens1, Joos Vandewalle1

Abstract:

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classific... In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classification problem. read more read less

Topics:

Least squares support vector machine (74%)74% related to the paper, Structured support vector machine (63%)63% related to the paper, Support vector machine (59%)59% related to the paper, Least squares (58%)58% related to the paper, Linear least squares (57%)57% related to the paper
View PDF
8,811 Citations
Journal Article DOI: 10.1023/A:1022995128597
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Jarmo Ilonen1, Joni-Kristian Kamarainen1, Jouni Lampinen1

Abstract:

An evolutionary optimization method over continuous search spaces, differential evolution, has recently been successfully applied to real world and artificial optimization problems and proposed also for neural network training However, differential evolution has not been comprehensively studied in the context of training neur... An evolutionary optimization method over continuous search spaces, differential evolution, has recently been successfully applied to real world and artificial optimization problems and proposed also for neural network training However, differential evolution has not been comprehensively studied in the context of training neural network weights, ie, how useful is differential evolution in finding the global optimum for expense of convergence speed In this study, differential evolution has been analyzed as a candidate global optimization method for feed-forward neural networks In comparison to gradient based methods, differential evolution seems not to provide any distinct advantage in terms of learning rate or solution quality Differential evolution can rather be used in validation of reached optima and in the development of regularization terms and non-conventional transfer functions that do not necessarily provide gradient information read more read less

Topics:

Evolution strategy (65%)65% related to the paper, Differential evolution (65%)65% related to the paper, Meta-optimization (62%)62% related to the paper, Evolutionary algorithm (60%)60% related to the paper, Feedforward neural network (59%)59% related to the paper
599 Citations
Journal Article DOI: 10.1007/BF02332159
Growing Grid — a self-organizing network with constant neighborhood range and adaptation strength
Bernd Fritzke1

Abstract:

We present a novel self-organizing network which is generated by a growth process. The application range of the model is the same as for Kohonen’s feature map: generation of topology-preserving and dimensionality-reducing mappings, e.g., for the purpose of data visualization. The network structure is a rectangular grid which,... We present a novel self-organizing network which is generated by a growth process. The application range of the model is the same as for Kohonen’s feature map: generation of topology-preserving and dimensionality-reducing mappings, e.g., for the purpose of data visualization. The network structure is a rectangular grid which, however, increases its size during self-organization. By inserting complete rows or columns of units the grid may adapt its height/width ratio to the given pattern distribution. Both the neighborhood range used to co-adapt units in the vicinity of the winning unit and the adaptation strength are constant during the growth phase. This makes it possible to let the network grow until an application-specific performance criterion is fulfilled or until a desired network size is reached. A final approximation phase with decaying adaptation strength finetunes the network. read more read less

Topics:

Grid (51%)51% related to the paper, Self-organizing map (51%)51% related to the paper
341 Citations
Journal Article DOI: 10.1023/A:1018647011077
The Fixed-Point Algorithm and Maximum Likelihood Estimation forIndependent Component Analysis
Aapo Hyvärinen1

Abstract:

The author previously introduced a fast fixed-point algorithm for independent component analysis. The algorithm was derived from objective functions motivated by projection pursuit. In this paper, it is shown that the algorithm is closely connected to maximum likelihood estimation as well. The basic fixed-point algorithm maxi... The author previously introduced a fast fixed-point algorithm for independent component analysis. The algorithm was derived from objective functions motivated by projection pursuit. In this paper, it is shown that the algorithm is closely connected to maximum likelihood estimation as well. The basic fixed-point algorithm maximizes the likelihood under the constraint of decorrelation, if the score function is used as the nonlinearity. Modifications of the algorithm maximize the likelihood without constraints. read more read less

Topics:

Maximum likelihood sequence estimation (66%)66% related to the paper, Expectation–maximization algorithm (65%)65% related to the paper, Difference-map algorithm (63%)63% related to the paper, Ramer–Douglas–Peucker algorithm (63%)63% related to the paper, Likelihood function (63%)63% related to the paper
300 Citations
Journal Article DOI: 10.1023/A:1013844811137
A Greedy EM Algorithm for Gaussian Mixture Learning
Nikos Vlassis1, Aristidis Likas2

Abstract:

Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get trapped in one of the many local maxima of the likelihood function. In this p... Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get trapped in one of the many local maxima of the likelihood function. In this paper we propose a greedy algorithm for learning a Gaussian mixture which tries to overcome these limitations. In particular, starting with a single component and adding components sequentially until a maximum number k, the algorithm is capable of achieving solutions superior to EM with k components in terms of the likelihood of a test set. The algorithm is based on recent theoretical results on incremental mixture density estimation, and uses a combination of global and local search each time a new component is added to the mixture. read more read less

Topics:

Local algorithm (63%)63% related to the paper, Mixture model (62%)62% related to the paper, Greedy algorithm (60%)60% related to the paper, Expectation–maximization algorithm (59%)59% related to the paper, Local search (optimization) (55%)55% related to the paper
View PDF
295 Citations
Author Pic

SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds
1
Choose a template
Select a template from a library of 40,000+ templates
2
Import a MS-Word file or start fresh
It takes only few seconds to import
3
View and edit your final output
SciSpace will automatically format your output to meet journal guidelines
4
Submit directly or Download
Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

clock Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

''

With SciSpace, you do not need a word template for Neural Processing Letters.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Turnitin Stats
Publisher Logos

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

publisher-logos

Easy support from all your favorite tools

Neural Processing Letters format uses SPBASIC citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Neural Processing Letters in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Neural Processing Letters guidelines and auto format it.

2. Do you follow the Neural Processing Letters guidelines?

Yes, the template is compliant with the Neural Processing Letters guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Neural Processing Letters?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Neural Processing Letters citation style.

4. Can I use the Neural Processing Letters templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Neural Processing Letters.

5. Can I use a manuscript in Neural Processing Letters that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Neural Processing Letters that you can download at the end.

6. How long does it usually take you to format my papers in Neural Processing Letters?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Neural Processing Letters.

7. Where can I find the template for the Neural Processing Letters?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Neural Processing Letters's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Neural Processing Letters's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Neural Processing Letters an online tool or is there a desktop version?

SciSpace's Neural Processing Letters is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Neural Processing Letters?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Neural Processing Letters?”

11. What is the output that I would get after using Neural Processing Letters?

After writing your paper autoformatting in Neural Processing Letters, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Neural Processing Letters's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Neural Processing Letters?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Neural Processing Letters. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Neural Processing Letters?

The 5 most common citation types in order of usage for Neural Processing Letters are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Neural Processing Letters?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Neural Processing Letters's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Neural Processing Letters in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Neural Processing Letters Endnote style according to Elsevier guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

Typset automatically formats your research paper to Neural Processing Letters formatting guidelines and citation style.

Verifed journal formats

One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

Andreas Frutiger
Researcher & Ex MS Word user
Use this template