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Search found 227 results on 10 pages for 'classification'.

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  • Classification of relationships in words?

    - by C.
    Hi, I'm not sure whats the best algorithm to use for the classification of relationships in words. For example in the case of a sentence such as "The yellow sun" there is a relationship between yellow and sun. THe machine learning techniques I have considered so far are Baynesian Statistics, Rough Sets, Fuzzy Logic, Hidden markov model and Artificial Neural Networks. Any suggestions please? thank you :)

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  • How to interpret weka classification?

    - by gargi2010
    How can we interpret the classification result in weka using naive bayes? How is mean, std deviation, weight sum and precision calculated? How is kappa statistic, mean absolute error, root mean squared error etc calculated? What is the interpretation of the confusion matrix?

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  • need help in site classification

    - by goh
    hi guys, I have to crawl the contents of several blogs. The problem is that I need to classify whether the blogs the authors are from a specific school and is talking about the school's stuff. May i know what's the best approach in doing the crawling or how should i go about the classification?

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  • Tracing or Logging Resource Governor classification function behavior in Sql Server 2008

    - by nganju
    I'm trying to use the Resource Governor in SQL Server 2008 but I find it hard to debug the classification function and figure out what the input variables will have, i.e. does SUSER_NAME() contain the domain name? What does the APP_NAME() string look like? It's also hard to verify that it's working correctly. What group did the function return? The only way I can see this is to fire up the performance monitor and watch unblinkingly for little blips in the right CPU counter. Is there some way I can either run it in Debug mode, where I can set a breakpoint and step through and look at variable values, or can I at least do the old-school method of writing trace statements to a file so I can see what's going on? Thanks...

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  • Bayes Misclassification error and plot : pattern recognition [closed]

    - by user1214586
    Below is a Matlab code for Bayes classifier which classifies arbitrary numbers into their classes. training = [3;5;17;19;24;27;31;38;45;48;52;56;66;69;73;78;84;88]; target_class = [0;0;10;10;20;20;30;30;40;40;50;50;60;60;70;70;80;80]; test = [1:2:90]'; class = classify(test,training, target_class, 'diaglinear'); % Naive Bayes classifier [test class] If someone could provide code snippets for calculating the Bayes error for misclassification and accuracy.Also, is it possible to plot a scatter plot and histogram indicating the number of data points belonging to different classes? Thank you.

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  • windows I/O manager - IRP's classification in read-like and write-like

    - by clyfe
    I am writing a windows filesystem minifilter driver that must fail IRP's in a preoperation callback. How can I find out from the callback parameters if the operation is read-like ( only reads data ) or it's write-like ( modifies data on the disk - write, delete etc ) ? I'm thinking on: Data->Iopb->TargetFileObject->ReadAccess Data->Iopb->TargetFileObject->WriteAccess But I'm not sure, I think thees are available only in postoperation callback. The documentation is really cumbersome. Code sample: FLT_PREOP_CALLBACK_STATUS Fail ( __inout PFLT_CALLBACK_DATA Data, __in PCFLT_RELATED_OBJECTS FltObjects, __deref_out_opt PVOID *CompletionContext ) { FLT_PREOP_CALLBACK_STATUS status = FLT_PREOP_SUCCESS_NO_CALLBACK; if ( IS WRITE_LIKE(Data, FltObjects) ) { // ??? HOW DO I FIND OUT???? if( FLT_IS_FASTIO_OPERATION(Data) ){ status = FLT_PREOP_DISALLOW_FASTIO; } else { status = FLT_PREOP_COMPLETE; } Data->IoStatus.Status = STATUS_ACCESS_DENIED; Data->IoStatus.Information = 0; return status; } return status; }

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  • Perceptron Classification and Model Training

    - by jake pinedo
    I'm having an issue with understanding how the Perceptron algorithm works and implementing it. cLabel = 0 #class label: corresponds directly with featureVectors and tweets for m in range(miters): for point in featureVectors: margin = answers[cLabel] * self.dot_product(point, w) if margin <= 0: modifier = float(lrate) * float(answers[cLabel]) modifiedPoint = point for x in modifiedPoint: if x != 0: x *= modifier newWeight = [modifiedPoint[i] + w[i] for i in range(len(w))] w = newWeight self._learnedWeight = w This is what I've implemented so far, where I have a list of class labels in answers and a learning rate (lrate) and a list of feature vectors. I run it for the numbers of iterations in miter and then get the final weight at the end. However, I'm not sure what to do with this weight. I've trained the perceptron and now I have to classify a set of tweets, but I don't know how to do that. EDIT: Specifically, what I do in my classify method is I go through and create a feature vector for the data I'm given, which isn't a problem at all, and then I take the self._learnedWeight that I get from the earlier training code and compute the dot-product of the vector and the weight. My weight and feature vectors include a bias in the 0th term of the list so I'm including that. I then check to see if the dotproduct is less than or equal to 0: if so, then I classify it as -1. Otherwise, it's 1. However, this doesn't seem to be working correctly.

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  • Image classification: recognizing various features of many buildings from images

    - by el chief
    so, let's say i have the long/lat or address of many buildings can get satellite images, "street view", and perhaps 3d/perspective views of buildings. want to find: height, number of floors, floor area (max building footprint) of the building. about 200k buildings. Is there a library for recognizing buildings from satellite shots or pictures? Kind of like face detection I suppose. Any other suggestions? Thanks!

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  • Micro Controller Serial Data identification or classification

    - by Posiedon
    I have a x51 family micro controller (P89V51RD2). I'm going to send some data from computer using serial port. The data i'll be sending are Character 'S' Character 'R' and a 2 digit integer. Upon receiving the data, I will be calling separate functions. I used if(chr=='S') and elseif(chr=='R')for character data. The main problem lies with identifying the 2 digit number sent. Any other data other than the above three mentioned will be discarded. Any ideas for identifying two digit integer ??

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  • Classification question

    - by adam
    If php and ruby are languages, and cake and rails are frameworks, how do CMS like drupal and joomla fit into the scheme... can you use them in any language and any framework?

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  • Classifying captured data in unknown format?

    - by monch1962
    I've got a large set of captured data (potentially hundreds of thousands of records), and I need to be able to break it down so I can both classify it and also produce "typical" data myself. Let me explain further... If I have the following strings of data: 132T339G1P112S 164T897F5A498S 144T989B9B223T 155T928X9Z554T ... you might start to infer the following: possibly all strings are 14 characters long the 4th, 8th, 10th and 14th characters may always be alphas, while the rest are numeric the first character may always be a '1' the 4th character may always be the letter 'T' the 14th character may be limited to only being 'S' or 'T' and so on... As you get more and more samples of real data, some of these "rules" might disappear; if you see a 15 character long string, then you have evidence that the 1st "rule" is incorrect. However, given a sufficiently large sample of strings that are exactly 14 characters long, you can start to assume that "all strings are 14 characters long" and assign a numeric figure to your degree of confidence (with an appropriate set of assumptions around the fact that you're seeing a suitably random set of all possible captured data). As you can probably tell, a human can do a lot of this classification by eye, but I'm not aware of libraries or algorithms that would allow a computer to do it. Given a set of captured data (significantly more complex than the above...), are there libraries that I can apply in my code to do this sort of classification for me, that will identify "rules" with a given degree of confidence? As a next step, I need to be able to take those rules, and use them to create my own data that conforms to these rules. I assume this is a significantly easier step than the classification, but I've never had to perform a task like this before so I'm really not sure how complex it is. At a guess, Python or Java (or possibly Perl or R) are possibly the "common" languages most likely to have these sorts of libraries, and maybe some of the bioinformatic libraries do this sort of thing. I really don't care which language I have to use; I need to solve the problem in whatever way I can. Any sort of pointer to information would be very useful. As you can probably tell, I'm struggling to describe this problem clearly, and there may be a set of appropriate keywords I can plug into Google that will point me towards the solution. Thanks in advance

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  • Feature Selection methods in MATLAB?

    - by Hossein
    Hi, I am trying to do some text classification with SVMs in MATLAB and really would to know if MATLAB has any methods for feature selection(Chi Sq.,MI,....), For the reason that I wan to try various methods and keeping the best method, I don't have time to implement all of them. That's why I am looking for such methods in MATLAB.Does any one know?

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  • Adaboost algorithm and its usage in face detection

    - by Hani
    I am trying to understand Adaboost algorithm but i have some troubles. After reading about Adaboost i realized that it is a classification algorithm(somehow like neural network). But i could not know how the weak classifiers are chosen (i think they are haar-like features for face detection) and how finally the H result which is the final strong classifier can be used. I mean if i found the alpha values and compute the H ,how am i going to benefit from it as a value (one or zero) for new images. Please is there an example describes it in a perfect way? i found the plus and minus example that is found in most adaboost tutorials but i did not know how exactly hi is chosen and how to adopt the same concept on face detection. I read many papers and i had many ideas but until now my ideas are not well arranged. Thanks....

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  • Inter-rater agreement (Fleiss' Kappa, Krippendorff's Alpha etc) Java API?

    - by adam
    I am working on building a Question Classification/Answering corpus as a part of my masters thesis. I'm looking at evaluating my expected answer type taxonomy with respect to inter-rater agreement/reliability, and I was wondering: Does anybody know of any decent (preferably free) Java API(s) that can do this? I'm reasonably certain all I need is Fleiss' Kappa and Krippendorff's Alpha at this point. Weka provides a kappa statistic in it's evaluation package, but I think it can only evaluate a classifier and I'm not at that stage yet (because I'm still building the data set and classes). Thanks.

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  • How to filter/sort/rank object model nodes?

    - by BCS
    I have some kind of object model and I need to filter and sort it's nodes for some kind of property. What kinds of automated systems exist to generate and select properties of the object model that correlate to what I want? (I'm intentionally being abstract and non-specific) I'm thinking of a system that works kind of like spam filters or supervised classification systems in that given an example data set it identifies rules that find nodes of interest. However I'm looking for a more general system in that it shouldn't require any design time information about the object model. It should work equality well as a spam filter on e-mail, a bug finder on a code base, an interest filter in a newsgroup or bot accounts finder on a social networking site. As long as it can explore the object model via reflection and be given a set of "interesting" nodes, it should be able to find rules that will find more nodes like them.

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  • Gradient boosting predictions in low-latency production environments?

    - by lockedoff
    Can anyone recommend a strategy for making predictions using a gradient boosting model in the <10-15ms range (the faster the better)? I have been using R's gbm package, but the first prediction takes ~50ms (subsequent vectorized predictions average to 1ms, so there appears to be overhead, perhaps in the call to the C++ library). As a guideline, there will be ~10-50 inputs and ~50-500 trees. The task is classification and I need access to predicted probabilities. I know there are a lot of libraries out there, but I've had little luck finding information even on rough prediction times for them. The training will happen offline, so only predictions need to be fast -- also, predictions may come from a piece of code / library that is completely separate from whatever does the training (as long as there is a common format for representing the trees).

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  • Issues in Convergence of Sequential minimal optimization for SVM

    - by Amol Joshi
    I have been working on Support Vector Machine for about 2 months now. I have coded SVM myself and for the optimization problem of SVM, I have used Sequential Minimal Optimization(SMO) by Mr. John Platt. Right now I am in the phase where I am going to grid search to find optimal C value for my dataset. ( Please find details of my project application and dataset details here http://stackoverflow.com/questions/2284059/svm-classification-minimum-number-of-input-sets-for-each-class) I have successfully checked my custom implemented SVM`s accuracy for C values ranging from 2^0 to 2^6. But now I am having some issues regarding the convergence of the SMO for C 128. Like I have tried to find the alpha values for C=128 and it is taking long time before it actually converges and successfully gives alpha values. Time taken for the SMO to converge is about 5 hours for C=100. This huge I think ( because SMO is supposed to be fast. ) though I`m getting good accuracy? I am screwed right not because I can not test the accuracy for higher values of C. I am actually displaying number of alphas changed in every pass of SMO and getting 10, 13, 8... alphas changing continuously. The KKT conditions assures convergence so what is so weird happening here? Please note that my implementation is working fine for C<=100 with good accuracy though the execution time is long. Please give me inputs on this issue. Thank You and Cheers.

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  • What algorithms are suitable for this simple machine learning problem?

    - by user213060
    I have a what I think is a simple machine learning question. Here is the basic problem: I am repeatedly given a new object and a list of descriptions about the object. For example: new_object: 'bob' new_object_descriptions: ['tall','old','funny']. I then have to use some kind of machine learning to find previously handled objects that had similar descriptions, for example, past_similar_objects: ['frank','steve','joe']. Next, I have an algorithm that can directly measure whether these objects are indeed similar to bob, for example, correct_objects: ['steve','joe']. The classifier is then given this feedback training of successful matches. Then this loop repeats with a new object. a Here's the pseudo-code: Classifier=new_classifier() while True: new_object,new_object_descriptions = get_new_object_and_descriptions() past_similar_objects = Classifier.classify(new_object,new_object_descriptions) correct_objects = calc_successful_matches(new_object,past_similar_objects) Classifier.train_successful_matches(object,correct_objects) But, there are some stipulations that may limit what classifier can be used: There will be millions of objects put into this classifier so classification and training needs to scale well to millions of object types and still be fast. I believe this disqualifies something like a spam classifier that is optimal for just two types: spam or not spam. (Update: I could probably narrow this to thousands of objects instead of millions, if that is a problem.) Again, I prefer speed when millions of objects are being classified, over accuracy. What are decent, fast machine learning algorithms for this purpose?

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  • string categorization strategies

    - by Andrew Heath
    I'm the one-man dev team on a fledgling military history website. One aspect of the site is a catalog of ~1,200 individual battles, including the nations & formations (regiments, divisions, etc) which took part. The formation information (as well as the other battle info) was manually imported from a series of books by a 10-man volunteer team. The formations were listed in groups with varying formatting and abbreviation patterns. At the time I set up the data collection forms I couldn't think of a good way to process that data... and elected to store it all as strings in the MySQL database and sort it out later. Well, "later" - as it tends to happen - has arrived. :-) Each battle has 2+ records in the database - one for each nation that participated. Each record has a formations text string listing the formations present as the volunteer chose to add them. Some real examples: 39th Grenadier Rgmt, 26th Volksgrenadier Division 2nd Luftwaffe Field Division, 246th Infantry Division 247th Rifle Division, 255th Tank Brigade 2nd Luftwaffe Field Division, SS Cavalry Division 28th Tank Brigade, 158th Rifle Division, 135th Rifle Division, 81st Tank Brigade, 242nd Tank Brigade 78th Infantry Division 3rd Kure Special Naval Landing Force, Tulagi Seaplane Base personnel 1st Battalion 505th Infantry Regiment The ultimate goal is for each individual force to have an ID, so that its participation can be traced throughout the battle database. Formation hierarchy, such as the final item above 1st Battalion (of the) 505th Infantry Regiment also needs to be preserved. In that case, 1st Battalion and 505th Infantry Regiment would be split, but 1st Battalion would be flagged as belonging to the 505th. In database terms, I think I want to pull the formation field out of the current battle info table and create three new tables: FORMATION [id] [name] FORMATION_HIERARCHY [id] [parent] [child] FORMATION_BATTLE [f_id] [battle_id] It's simple to explain, but complicated to enact. What I'm looking for from the SO community is just some tips on how best to tackle this problem. Ideally there's some sort of method to solving this that I'm not aware of. However, as a last resort, I could always code a classification framework and call my volunteers back to sort through 2,500+ records...

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  • what is the best way to generate fake data for classification problem ?

    - by Berkay
    i'm working on a project and i have a subset of user's key-stroke time data.This means that the user makes n attempts and i will use these recorded attempt time data in various kinds of classification algorithms for future user attempts to verify that the login process is done by the user or some another person. (Simply i can say that this is biometrics) I have 3 different times of the user login attempt process, ofcourse this is subset of the infinite data. until now it is an easy classification problem, i decided to use WEKA but as far as i understand i have to create some fake data to feed the classification algorithm. can i use some optimization algorithms ? or is there any way to create this fake data to get min false positives ? Thanks

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  • Mahout Naive Bayes Classifier for Items

    - by Nimesh Parikh
    Team, I am working on a project where i need to classify Items into certain category. I have a single file as input; which contains target variable and space separated features. My training data will look like Category Name [Tab] DataString Plumbing [Tab] Pipe Tap Plastic Pipe PVC Pipe Cold Water Line Hot Water Line Tee outlet up Elbow turned up Elbow turned down Gate valve Globe valve Paint [Tab] Ivory Black Burnt Umber Caput Mortuum Violet Earth Red Yellow Ochre Titanium White Cadmium Yellow Light Cadmium Yellow Deep Cloths [Tab] Shirt T-Shirt Pent Jeans Tee Cargo Well, I have really big set of Category. I have couple of question here am i using correct data for Training? If no then what should i use? Once I train and Test my model, what is next step? How can i use output? Please help me with this Thanks, Nimesh

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