similar to: call for paper-nov/dec 2010

Dear Sir/Madam *CALL FOR PAPER – JANUARY / FEBRUARY ISSUE* Greetings from INTERNATIONAL JOURNAL OF COMPUTER TECHNOLOGY AND APPLICATIONS (IJCTA) IJCTA is an international, peer-reviewed online journal entitled to publish original research articles in the fields of Computer science and Information technology *Topics* The articles shall cover the following topics but not limited to Computer

[Apologies if you receive multiple copies of this Call For Papers.] CALL FOR PAPERS ================= The 4th International Conference on Information and Communication Systems, ICICS 2013 Organized by Jordan University of Science and Technology April 23-25 2013, Irbid, Jordan http://www.icics.info/icics2013 IMPORTANT DATES ================= Full Paper Submission: Dec. 1st, 2012 Notification of

Dave, I have been interested in some of the same questions that you have been asking. A while back I asked a person at VLSI ( http://www.vlsi.fi/ ) in Finland if they had a product that might work. The exchange of email is included below. In summary, the answer was probably no, but maybe in the future. The VS1005 chip he mentions does not seem to have appeared yet. I currently have a pair

Hello Sir/Madam, i am interested to send the paper in the R journal please provide me the information of address submission so i can easily send the paper to the editor of chief. [[alternative HTML version deleted]]

Greetings, Normally, we hold the Xen Project Documentation Day on the fourth Monday of the month. However, for the next two months, there are some issues: - Next Monday is the beginning of Thanksgiving week in the US. This is historically a very hard time to accomplish anything (other than shop for the holidays which occur in December), so if we hold Documentation Day on Monday, it is likely

To the R users community: (We apologize for multiples copies) (Please distribute) --------------------------------------------------------------------------- 1st Call for Papers - 2nd International Symposium on Distributed Computing and Artificial Intelligence (DCAI'09) June 10th - 12th , 2009 - Salamanca - Spain http://dcai.usal.es/ The International Symposium on Distributed Computing and

Hello! The Array Programming Languages Conference will be from 24th - 27th July 2000 Please look at: http://stat.cs.tu-berlin.de/APL-Berlin-2000/ Chairman's Address APL Berlin 2000 will take place in a city which is worth a visit even without array processing languages. There is a fortunate coincidence: Much activity in scientific and commercial institutions is based on such languages.

Hi, I'm working on a classification problem and one of the methods I'd like to use are neural networks. I've been using nnet to build a classification network. However I would like to have the probabilities associated with the prediction. Are there any implementations of probabilistic neural networks available in R? thanks,

Hello, I am Mohiuddin Abdul Qader, final year student from dept of CSE in Bangladesh University of Engineering & Technology(BUET). My major was artificial intelligence & i finished my course on Machine Learning and Pattern Recognition this year. I am very keen to contribute in open source community. I have just completed my thesis on 'Location Based Structured Web Search'. For the

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

******************************************************************** ICANN 2008 EXTENDED SUBMISSION DEADLINE MARCH 10 2008 ******************************************************************** Dear Colleague Due to numerous requests we decided to extend the submission deadline for ICANN 2008 to March 10, 2008 for regular papers and to March 20, 2008 for papers intended for

The TITLE for the nnet package is garbled: it comes out as nnet Feed-forward neural networks and multinomial log-linear nnet Feed-forward neural networks and multinomial log-linear models The problem is in maketitle: auk% cat DESCRIPTION Bundle: VR Version: 6.1-9 Date: 2000/07/11 Depends: R (>= 1.1) Author: S original by Venables & Ripley. R port by Brian Ripley

Hi, I am starting to play around with neural networks and noticed that there are several packages on the CRAN website for neural networks (AMORE, grnnR, neural, neuralnet, maybe more if I missed them). Are any of these packages more well-suited for newbies to neural networks? Are there any relative strengths / weaknesses to the different implementations? If anyone has any advice before I dive

Hi. I started with a file which was a sparse 982x923 matrix and where the last column was a variable to be predicted. I did principle component analysis on it and arrived at a new 982x923 matrix. Then I ran the code below to get a neural network using nnet and then wanted to get a confusion matrix or at least know how accurate the neural net was. I used the first 22 principle components only for

Setting: 200 input variables, 1 binary target variable. Run a principle component analysis on the data then use the output of the principle component analysis (the generated factors) as input into a neural network -but first having partitioned the pca data into training and testing sets so that a neural network model can be trained on the first partition and tested on the second. I was told that

Hi, I am trying to build a VERY basic neural network as a practice before hopefully increasing my scope. To do so, I have been using package "neural" and the MLP related functions (mlp and mlptrain) within that package. So far, I have created a basic network, but I have been unable to change the default activation function. If someone has a suggestion, please advise. The goal of the

Hi all, I am trying to develop a neural network with single target variable and 5 input variables to predict the importance of input variables using R. I used the packages nnet and RSNNS. But unfortunately I could not interpret the out put properly and the documentation of that packages also not giving proper direction. Please help me to find a good package with a proper documentation for neural