similar to: Classification

Hello to all: When I make a call of extension (FXS) to extension (FXS) (TDM40B) status of the call is the following one: #### Case call answered --------------------------------------------------- Log init -- Starting simple switch on 'Zap/4-1' -- Executing Macro("Zap/4-1", "stdexten|103|Zap/2") in new stack -- Executing Dial("Zap/4-1",

Hi Lists, I have one box with two FXO and two FXS ports, it is running asterisk inside. I have one sinle POTS line connected to the one FXO and two phone sets connected to the FXS port. Extension 6003 is asigned to one fxs and 6004 is asigned to another fxs, the two extensions can call each other, they can both make/receive PSTN call, but they can't make PSTN call simultaneously. Is it

Hi all, Another requirement we have is that traffic entering the DS domain be classified then subsequently assigned a (different?) DSCP based upon its classification. For illustrative purposes only let''s say (for traffic entering the DS domain on dev eth0): - WWW traffic would be marked BE - traffic destined for 10.10.10.10 would be marked AF11 - VoIP traffic from 20.20.20.20 would

Dear All, I have a data, suppose it is an N*M matrix data. All I want is to classify it into, let see, 3 classes. Which method(s) do you think is(are) appropriate for this purpose? Any reference will be welcome! Thanks! Best, Baoqiang Cao

Hello! I need some help with the linear discriminant analysis in R. I have some plant samples (divided into several groups) on which I measured a few quantitative characteristics. Now, I need to infer some classification rules usable for identifying new samples. I have used the function lda from the MASS library in a usual fashion: lda.1 <- lda(groups~char1+char2+char3, data=xxx) I'd

Hello, I'm attempting to classify data using tree(). summary(tree()) indicates that I have a very good classification rate. What I'd like to know is which tokens in the data set are correctly classified and which are not. Is there a method for associating the classification with the token? I've been reading Chambers and Hastie (1992) chapter 9 on tree-based models, but find no

Dear list, there are now >400 packages available on CRAN. Would it be useful to classify these packages according to what they do (e.g. classification, graphics, spatial statistics), to assist the user in finding the appropriate package for their problem? Or perhaps the search facility is enough. I would attempt such a classification, but my knowledge of statistical methods isn't good

Dear All, I'm classifying some data with various methods (binary classification). I'm interpreting the results via a confusion matrix from which I calculate the sensitifity and the fdr. The classifiers are trained on 575 data points and my test set has 50 data points. I'd like to calculate p-values for obtaining <=fdr and >=sensitifity for each classifier. I was thinking about

I've been groping my way through a classification/discrimination problem, from a consulting client. There are 26 observations, with 4 possible categories and 24 (!!!) potential predictor variables. I tried using lda() on the first 7 predictor variables and got 24 of the 26 observations correctly classified. (Training and testing both on the complete data set --- just to get started.) I

Hola! Este mail llegó a r-help hoy, yo lo mandó también a esta lista. Es interesante con competiciones de predicción/clasificación! ¿Alguien que quiere cooperar? Kjetil ---------- Forwarded message ---------- From: Kjetil Halvorsen <kjetilbrinchmannhalvorsen@gmail.com> Date: Fri, Jan 29, 2010 at 12:19 Subject: Fwd: [R] Classification of supernovae - a challenge To: Kjetil Halvorsen

Hello, Could anyone tell me what rules R uses to classify an autoregressive model i.e. how it decides the number of AR parameters to use when the AR(data) function is applied? I understood that model classification was more or less based on inspection of the acf and pacf functions. Thanks TT -- View this message in context:

Hi All, Sorry if this is a repost (a quick browse didn't give me the answer). I wonder if there are packages that can do the feature selection and classification at the same time. For instance, I am using SVM to classify my samples, but it's easy to get overfitted if using all of the features. Thus, it is necessary to select "good" features to build an optimum hyperplane (?).

Hi, I want using zero-inflated negative binomial regression model to classify data(a vector of data), that is I want know each observed value is more likely belong to the "zero" or "count" distribution(better with relative probability). My data is some like: count site samp 12909 1 1 602 1 2 50 1 3 1218 1 4 91291 1 5

Hi I am Aadhithya I am trying to write a code to classify microarray data (AML and ALL) using SVM in R my code goes like this : library(e1071) train<-read.table("Z:/Documents/train.txt",header=T); test<-read.table("Z:/Documents/test.txt",header=T); cl <- c(c(rep("ALL",10), rep("AML",10))); model<- svm(train,cl); pred <-

Hi, we are using asterisk with Junghanns QuadBri and some sip phones. 2 channels are configured in NT mode (ISDN PBX connected, internal ) and 2 channels are connected to the public ISDN network (bri-cpe). We use Bristuff 0.2.0 RC8C from Junghanns. When a call comes in from the public phone for a specific extension (Hotline Number), we initiate a parallelcall to some SIP phones and also to our

Hello everyone, I was looking for a way to classify time-series based on the curve-fit. I try to campute several trends so i was thinking to link each trend with a function. increase with exponential for example, increase and decrease with a gaussian etc. The possiblities are endless though and it seems that is not always working well, especially if you work on small time-series (of 5-10 points

Hi! I am doing a course about the R software and I have a couple of doubts. In one of the tasks we were asigned, we have to perform a function that simulates a bus' trip with 25 stops. In each of those stops, between 0 and 6 people can go on board. And when the total of passengers reaches 44, no more people can go on board. stops <- 25 passengers <- 0 register <- numeric(25) bus

Dear all, i searched for some classification methods and I have no glue if i took the right once. My problem: I have a matrix with 17000 rows and 33 colums (genes and patients). The patients are grouped into 3 diseases. No I want to classify the patients and for sure i want to know which rows are more helpful for the classification than others. I tried SVM and random forest. Do you think this

I recently use gbm for a binary classification problem. As expected, it gets very good results, based on Area under ROC with 7-fold cross validation. However, the application (malware detection) is cost-sensitive, getting a FP (classify a clean sample as a dirty one) is much worse than getting a FN (miss a dirty sample). I would like to tune the gbm model biased to very low FP rate. For this

Hi, everyone, I haven't found anything similar in the forum, so here's my problem (I'm no expert in R nor statistics): I have a data set of 59.000 cases with 9 variables each (fractional coverage of 9 different plant types, such as deciduous broad-leaved temperate trees or evergreen tropical trees etc.), which was generated by a vegetation model. In order to evaluate the quality of