The learning example of the DeepBelief framework demonstrates how to train a neural network to recognize one object category. The method used for training jpcnn_train() does not have a category label parameter.
However, in the DeepBelief simple example, the given neural network can categorize multiple object categories. Is there a way to do that kind of training through DeepBelief? Or should I look in to Caffe and use that instead as DeepBelief is based on Caffe?
Based on their documentation, in particular on a docs for functions jpcnn_train and jpcnn_predict, it does not appear to support multiclass classification for custom labels out of the box. It does seem to support multiclass classification for ImageNet labels.
However, you can train multiple predictors (here's how to train one), one per your custom class, and then choose the class for which the corresponding predictor outputs the highest value.
I'm using weka for some classification experiments. i was trying some of the features provided by weka that can be applied on extracted attributes, and I found that applying clustermembership on the attributes will provide relatively higher accuracy than other methods. I'm not quite sure what this feature does since it removes all the attributes and only keeps something like pCluster_0_0 , pCluster_1_0 , pCluster_2_0 and the class-attribute.So I'm not quite sure the results that I'm getting from this is valid and will it work for other new unseen instances. From Weka documentations
A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data). If a (nominal) class attribute is set, the clusterer is run separately for each class. The class attribute (if set) and any user-specified attributes are ignored during the clustering operation.
I do appreciate any help to understand this.
It basically does what the documentation you read describes! It uses a clustering algorithm to get the cluster membership of each input instance (i.e. the cluster the instance belongs in) and outputs them as new instances. A word of caution that the clustering algorithm used must be a density based clusterer, so DBSCAN or expectation maximisation for instance.
As for if your result is valid, you will need to run a test set against the clusterer or do percentage split evaluation. You could be overfitting your data!
I am using the classical SIFT - BOW - SVM for image classification. My classifiers are created using the 1vsAll paradigm.
Let's say that I currently have 100 classes.
Later, I would like to add new classes OR I would like to improve the recognition for some specific classes using additional training sets.
What would be the best approach to do it ?
Of course, the best way would be to re-execute every steps of the training stage.
But would it make any sense to only compute the additional (or modified) classes using the same vocabulary as the previous model, in order avoid to recompute a new vocabulary and train again ALL the classes ?
Thanks
In short - no. If you add new class it should be added to each of the "old" classifiers so "one vs. all" still makes sense. If you assume that new classes can appear with time consider using one class classifiers instead, such as one-class SVM. This way once you get new samples for a particular class you only retrain a particular model, or add a completely new one to the system.
Furthermore, for large number of classes, 1 vs all SVM works quite badly, and one-class approach is usually much better.
I have a huge data set and would like to do a multi-lable classification where each object can be assigned to more than one class. I'm using a Naive Bayer Classifier in Apache Mahout to do that. However it is not designed for multi-lable classification and just assign class with highest probability to each object. How can I extend this classifier to my scenario?
One solution that I was thinking was to put a threshold and assign classes whose probabilities are larger than the threshold. But it is not easy to find the threshold so it does not work. I wonder to know if any one has any idea?
You need to train a binary classifier for each class. Train set should contain data with target class and other arbitrary data not matching the target class.
In a particular application I was in need of machine learning (I know the things I studied in my undergraduate course). I used Support Vector Machines and got the problem solved. Its working fine.
Now I need to improve the system. Problems here are
I get additional training examples every week. Right now the system starts training freshly with updated examples (old examples + new examples). I want to make it incremental learning. Using previous knowledge (instead of previous examples) with new examples to get new model (knowledge)
Right my training examples has 3 classes. So, every training example is fitted into one of these 3 classes. I want functionality of "Unknown" class. Anything that doesn't fit these 3 classes must be marked as "unknown". But I can't treat "Unknown" as a new class and provide examples for this too.
Assuming, the "unknown" class is implemented. When class is "unknown" the user of the application inputs the what he thinks the class might be. Now, I need to incorporate the user input into the learning. I've no idea about how to do this too. Would it make any difference if the user inputs a new class (i.e.. a class that is not already in the training set)?
Do I need to choose a new algorithm or Support Vector Machines can do this?
PS: I'm using libsvm implementation for SVM.
I just wrote my Answer using the same organization as your Question (1., 2., 3).
Can SVMs do this--i.e., incremental learning? Multi-Layer Perceptrons of course can--because the subsequent training instances don't affect the basic network architecture, they'll just cause adjustment in the values of the weight matrices. But SVMs? It seems to me that (in theory) one additional training instance could change the selection of the support vectors. But again, i don't know.
I think you can solve this problem quite easily by configuring LIBSVM in one-against-many--i.e., as a one-class classifier. SVMs are one-class classifiers; application of an SVM for multi-class means that it has been coded to perform multiple, step-wise one-against-many classifications, but again the algorithm is trained (and tested) one class at a time. If you do this, then what's left after step-wise execution against the test set, is "unknown"--in other words, whatever data is not classified after performing multiple, sequential one-class classifications, is by definition in that 'unknown' class.
Why not make the user's guess a feature (i.e., just another dependent variable)? The only other option is to make it the class label itself, and you don't want that. So you would, for instance, add a column to your data matrix "user class guess", and just populate it with some value most likely to have no effect for those data points not in the 'unknown' category and therefore for which the user will not offer a guess--this value could be '0' or '1', but really it depends on how you have your data scaled and normalized).
Your first item will likely be the most difficult, since there are essentially no good incremental SVM implementations in existence.
A few months ago, I also researched online or incremental SVM algorithms. Unfortunately, the current state of implementations is quite sparse. All I found was a Matlab example, OnlineSVR (a thesis project only implementing regression support), and SVMHeavy (only binary class support).
I haven't used any of them personally. They all appear to be at the "research toy" stage. I couldn't even get SVMHeavy to compile.
For now, you can probably get away with doing periodic batch training to incorporate updates. I also use LibSVM, and it's quite fast, so it sould be a good substitute until a proper incremental version is implemented.
I also don't think SVM's can model the concept of an "unknown" sample by default. They typically work as a series of boolean classifiers, so a sample ends up as positively being classified as something, even if that sample is drastically different from anything seen previously. A possible workaround would be to model the ranges of your features, and randomly generate samples that exist outside of these ranges, and then add these to your training set.
For example, if you have an attribute called "color", which has a minimum value of 4 and a maximum value of 123, then you could add these to your training set
[({'color':3},'unknown'),({'color':125},'unknown')]
to give your SVM an idea of what an "unknown" color means.
There are algorithms to train an SVM incrementally, but I don't think libSVM implements this. I think you should consider whether you really need this feature. I see no problem with your current approach, unless the training process is really too slow. If it is, could you retrain in batches (i.e. after every 100 new examples)?
You can get libSVM to produce probabilities of class membership. I think this can be done for multiclass classification, but I'm not entirely sure about that. You will need to decide some threshold at which the classification is not certain enough and then output 'Unknown'. I suppose something like setting a threshold on the difference between the most likely and second most likely class would achieve this.
I think libSVM scales to any number of new classes. The accuracy of your model may well suffer by adding new classes, however.
Even though this question is probably out of date, I feel obliged to give some additional thoughts.
Since your first question has been answered by others (there is no production-ready SVM which implements incremental learning, even though it is possible), I will skip it. ;)
Adding 'Unknown' as a class is not a good idea. Depending on it's use, the reasons are different.
If you are using the 'Unknown' class as a tag for "this instance has not been classified, but belongs to one of the known classes", then your SVM is in deep trouble. The reason is, that libsvm builds several binary classifiers and combines them. So if you have three classes - let's say A, B and C - the SVM builds the first binary classifier by splitting the training examples into "classified as A" and "any other class". The latter will obviously contain all examples from the 'Unknown' class. When trying to build a hyperplane, examples in 'Unknown' (which really belong to the class 'A') will probably cause the SVM to build a hyperplane with a very small margin and will poorly recognizes future instances of A, i.e. it's generalization performance will diminish. That's due to the fact, that the SVM will try to build a hyperplane which separates most instances of A (those officially labeled as 'A') onto one side of the hyperplane and some instances (those officially labeled as 'Unknown') on the other side .
Another problem occurs if you are using the 'Unknown' class to store all examples, whose class is not yet known to the SVM. For example, the SVM knows the classes A, B and C, but you recently got example data for two new classes D and E. Since these examples are not classified and the new classes not known to the SVM, you may want to temporarily store them in 'Unknown'. In that case the 'Unknown' class may cause trouble, since it possibly contains examples with enormous variation in the values of it's features. That will make it very hard to create good separating hyperplanes and therefore the resulting classifier will poorly recognize new instances of D or E as 'Unknown'. Probably the classification of new instances belonging to A, B or C will be hindered as well.
To sum up: Introducing an 'Unknown' class which contains examples of known classes or examples of several new classes will result in a poor classifier. I think it's best to ignore all unclassified instances when training the classifier.
I would recommend, that you solve this issue outside the classification algorithm. I was asked for this feature myself and implemented a single webpage, which shows an image of the object in question and a button for each known class. If the object in question belongs to a class which is not known yet, the user can fill out another form to add a new class. If he goes back to the classification page, another button for that class will magically appear. After the instances have been classified, they can be used for training the classifier. (I used a database to store the known classes and reference which example belongs to which class. I implemented an export function to make the data SVM-ready.)