I have a task i need to solve using computer vision, but I can't figure out what kind of feature would be ideal to extract. I could possibly train a CNN network, which could do the classification, is it then somehow possible to learn what kind of features it uses, and what is used to discriminate class A from class b?
And then do the same just using openCV or something similar?
Yes, you can do that. Actually, that is exactly what a CNN does, they learn features that might be the best for your particular data and task, and then you can use a trained network as a feature extractor.
This is pretty common use of a CNN, and OpenCV has some limited support for neural networks in the DNN module.
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I'm trying to build an app that makes suggestions (distinct classes) based on a table with 4 features: latitude, longitude, time and weekday.
The training data of my app is 100% personal, so it doesn't really make sense to pre-train the model. I wanna be able to train on device. I know CoreML 3 supports updating for neural networks and kNN classifiers, but does this really help me with my tabular data?
Other tabular classifiers like boasted tree, random forest... can't be trained on device unfortunately. Are there alternatives to CoreML for on device training of those simpler machine learning algorithms? Or can CoreML somehow already do what I want.
Unfortunately I'm not really an expert in neural networks.
Just because Core ML doesn't provide something, doesn't mean it's impossible. :-) You can use existing libraries or implement the algorithm by yourself.
If you're looking to build a logistic regression classifier, this is fairly easy to implement by hand. (You can even use a neural network with a single layer for this and still use Core ML.)
Is it possible to feed image features, say SIFT features, to a convolutional neural network model in Tensorflow? I am trying a tensorflow implementation of this project in which a grayscale image is coloured. Will image features be a better choice than feeding the images as is to the model?
PS. I am a novice to machine learning and is not familiar with creating neural n/w models
You can feed tensorflow neural net almost anything.
If you have extra features for each pixel, then instead of using one channel (intensity) you would use multiple channels.
If you have extra features, which are about whole image, you can make separate input a merge features at some upper layer.
As for the better performance, you should try both approaches.
General intuition is that, extra features help if you don't have many samples and their effect is diminishing if you have many samples and network can learn features by itself.
Also one more point: If you are novice, I strongly recommend using higher level framework like keras.io (which is layer over tensorflow) instead of tensorflow.
Does it make any sense to perform feature extraction on images using, e.g., OpenCV, then use Caffe for classification of those features?
I am asking this as opposed to the traditional way of passing the images directly to Caffe, and letting Caffe do the extraction and classification procedures.
Yes, it does make sense, but it may not be the first thing you want to try:
If you have already extracted hand-crafted features that are suitable for your domain, there is a good chance you'll get satisfactory results by using an easier-to-use machine learning tool (e.g. libsvm).
Caffe can be used in many different ways with your features. If they are low-level features (e.g. Histogram of Gradients), then several convolutional layers may be able to extract the appropriate mid-level features for your problem. You may also use caffe as an alternative non-linear classifier (instead of SVM). You have the freedom to try (too) many things, but my advice is to first try a machine learning method with a smaller meta-parameter space, especially if you're new to neural nets and caffe.
Caffe is a tool for training and evaluating deep neural networks. It is quite a versatile tool allowing for both deep convolutional nets as well as other architectures.
Of course it can be used to process pre-computed image features.
Hey I have a task to perform, which is basically to somehow retrieve powerpoint presentations or pdf documents pertaining to a certain field. Let's say I want to retrieve ppt and pdf lecture notes pertaining to bioinformatics field. I would like to know if this task can be achieved by adapting the approach of using neural bots trained by a neural network? Just wanted to confirm that this approach is not completely wrong before I proceeded further with my implementation.
And in case someone is wondering why a neural network or any learning algorithm at all is required in this case well here is my plan (which might be wrong or there might be an easier way to achieve this so please feel free to correct me):
I generate neural bots trained by a neural network (not sure how this training happens yet, I am assuming by supervised learning using a sample training set of certain ppt and pdf files) and then these bots retrieve pages that are similar to what they learnt through their training.
So is the above approach a correct way to go about completing this task?
Neural nets are complicated. It seems like you have a generic document classification problem. The simplest place to start is using some kind of naive bayes model with bag of word features. The next step I'd take from there is to use a linear SVM or logistic regression on the same feature set. If you still don't have the performance you want after you tried simpler things, maybe then go on to try using neural nets.
Just like you wouldn't say, I want to do write an email server, I'll start by writing an operating system, I'd tend to be wary of using neural nets before simpler things have failed.
I am doing a project where I have neural networks (or other algorithms) play each other in poker. After each win or loss, I want the neural network (or other algorithm) to update in response to the error of the loss (how this is calculated is unimportant here).
Weka is very nice and I don't want to reinvent the wheel. However, Weka's API seems primarily designed to train from a dataset. Game playing doesn't use a dataset. Rather, the network plays, and then I want it to update itself based on its loss.
Is it possible to use the Weka API to update a network instead of a dataset but on one instance and do this over and over again? I'm I thinking about this right?
The other idea I also want to implement is use a genetic algorithm to update the weights in a neural network, instead of the backpropogation algorithm. As far as I can tell, there is no way to manually specify the weights of a neural network in Weka. This, of course, is vital if using a genetic algorithm for this purpose.
Please help :) Thank you.
Normally weka learning algorithms are batch learning algoritms. What you need are incremental classifier.
From weka docs
Most classifiers need to see all the data before they can be trained, e.g., J48 or SMO. But there are also schemes that can be trained in an incremental fashion, not just in batch mode. All classifiers implementing the weka.classifiers.UpdateableClassifier interface are able to process data in such a way.
See UpdateableClassifier interface to which classifiers implement it.
Also you may look MOA Massive Online Analysis tool which is closely related with weka and all of its classifiers are incremental due to constraints of online learning.
Weka, as far as I can tell, does not do online learning (which is what you're asking about).
It might be better to investigate using competitive analysis for your game.
You may have to reinvent the wheel here. I don't think it's a bad use of time.
I'm currently implementing a learning classifier system, which is pretty simple. I'd also advise looking into these kinds of algorithms. There is an implementation on the internet, but I still prefer to code my own.