it is my first time dealing with audio data and i'm having a bit of trouble extracting the freature of a real sample audio to pass on the model for evaluation especially that i didn't really understand how the data was pre-processed from the paper below and "The exact order of appearance of the features is not known"
as stated in the Attribute Information.
https://aclanthology.org/H90-1075.pdf
any help would be appreciated.
i tried searching for "spectral coefficients; contour features, sonorant features, pre-sonorant features, and post-sonorant features" but got with a lot of information to start with.
Related
I'm new to the ML/NLP field so my question is what technology would be most appropriate to achieve the following goal:
We have a short sentence - "Where to go for dinner?" or "What's your favorite bar?" or "What's your favorite cheap bar?"
Is there a technology that would enable me to train it providing the following data sets:
"Where to go for dinner?" -> Dinner
"What's your favorite bar?" -> Bar
"What's your favorite cheap restaurant?" -> Cheap, Restaurant
so that next time we have a similar question about an unknown activity, say, "What is your favorite expensive [whatever]" it would be able to extract "expensive" and [whatever]?
The goal is if we can train it with hundreds of variations(or thousands) of the question asked and relevant output data expected, so that it can work with everyday language.
I know how to make it even without NLP/ML if we have a dictionary of expected terms like Bar, Restaurant, Pool, etc., but we also want it to work with unknown terms.
I've seen examples with Rake and Scikit-learn for classification of "things", but I'm not sure how would I feed text into those and all those examples had predefined outputs for training.
I've also tried Google's NLP API, Amazon Lex and Wit to see how good they are at extracting entities, but the results are disappointing to say the least.
Reading about summarization techniques, I'm left with the impression it won't work with small, single-sentence texts, so I haven't delved into it.
As #polm23 mentioned for simple stuff you can use the POS tagging to do the extraction. The services you mentioned like LUIS, Dialog flow etc. , uses what is called Natural Language Understanding. They make uses of intents & entities(detailed explanation with examples you can find here). If you are concerned that your data is going online or sometimes you have to go offline, you always go for RASA.
Things you can do with RASA:
Entity extraction and sentence classification. Mention which particular term to be extracted from the sentence by tagging the word position with a variety of sentence. So if any different word comes other than what you had given in the training set it will be detected.
Uses rule-based learning and also keras LSTM for detection.
One downside when comparing with the online services is that you have to manually tag the position numbers in the JSON file for training as opposed to the click and tag features in the online services.
You can find the tutorial here.
I am having pain in my leg.
Eg I have trained RASA with a variety of sentences for identifying body part and symptom (I have limited to 2 entities only, you can add more), then when an unknown sentence (like the one above) appears it will correctly identify "pain" as "symptom" and "leg" as "body part".
Hope this answers your question!
Since "hundreds to thousands" sound like you have very little data for training a model from scratch. You might want to consider training (technically fine-tuning) a DialogFlow Agent to match sentences ("Where to go for dinner?") to intents ("Dinner"), then integrating via API calls.
Alternatively, you can invest time in fine-tuning a small pre-trained model like "Distilled BERT classifier" from "HuggingFace" as you won't need the 100s of thousands to billions of data samples required to train a production-worthy model. This can also be assessed offline and will equip you to solve other NLP problems in the future without much low-level understanding of the underlying statistics.
I have developed a ML model for a classification (0/1) NLP task and deployed it in production environment. The prediction of the model is displayed to users, and the users have the option to give a feedback (if the prediction was right/wrong).
How can I continuously incorporate this feedback in my model ? From a UX stand point you dont want a user to correct/teach the system more than twice/thrice for a specific input, system shld learn fast i.e. so the feedback shld be incorporated "fast". (Google priority inbox does this in a seamless way)
How does one build this "feedback loop" using which my system can improve ? I have searched a lot on net but could not find relevant material. any pointers will be of great help.
Pls dont say retrain the model from scratch by including new data points. Thats surely not how google and facebook build their smart systems
To further explain my question - think of google's spam detector or their priority inbox or their recent feature of "smart replies". Its a well known fact that they have the ability to learn / incorporate (fast) user feed.
All the while when it incorporates the user feedback fast (i.e. user has to teach the system correct output atmost 2-3 times per data point and the system start to give correct output for that data point) AND it also ensure it maintains old learnings and does not start to give wrong outputs on older data points (where it was giving right output earlier) while incorporating the learning from new data point.
I have not found any blog/literature/discussion w.r.t how to build such systems - An intelligent system that explains in detaieedback loop" in ML systems
Hope my question is little more clear now.
Update: Some related questions I found are:
Does the SVM in sklearn support incremental (online) learning?
https://datascience.stackexchange.com/questions/1073/libraries-for-online-machine-learning
http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/
https://en.wikipedia.org/wiki/Concept_drift
Update: I still dont have a concrete answer but such a recipe does exists. Read the section "Learning from the feedback" in the following blog Machine Learning != Learning Machine. In this Jean talks about "adding a feedback ingestion loop to machine". Same in here, here, here4.
There could be couple of ways to do this:
1) You can incorporate the feedback that you get from the user to only train the last layer of your model, keeping the weights of all other layers intact. Intuitively, for example, in case of CNN this means you are extracting the features using your model but slightly adjusting the classifier to account for the peculiarities of your specific user.
2) Another way could be to have a global model ( which was trained on your large training set) and a simple logistic regression which is user specific. For final predictions, you can combine the results of the two predictions. See this paper by google on how they do it for their priority inbox.
Build a simple, light model(s) that can be updated per feedback. Online Machine learning gives a number of candidates for this
Most good online classifiers are linear. In which case we can have a couple of them and achieve non-linearity by combining them via a small shallow neural net
https://stats.stackexchange.com/questions/126546/nonlinear-dynamic-online-classification-looking-for-an-algorithm
This is my problem description:
"According to the Survey on Household Income and Wealth, we need to find out the top 10% households with the most income and expenditures. However, we know that these collected data is not reliable due to many misstatements. Despite these misstatements, we have some features in the dataset which are certainly reliable. But these certain features are just a little part of information for each household wealth."
Unreliable data means that households tell lies to government. These households misstate their income and wealth in order to unfairly get more governmental services. Therefore, these fraudulent statements in original data will lead to incorrect results and patterns.
Now, I have below questions:
How should we deal with unreliable data in data science?
Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
-How can we evaluate our errors in this study? Since we have unlabeled dataset, should I look for labeling techniques? Or, should I use unsupervised methods? Or, should I work with semi-supervised learning methods?
Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
Please introduce me any ideas or references which can help me in this issue.
Thanks in advance.
Q: How should we deal with unreliable data in data science
A: Use feature engineering to fix unreliable data (make some transformations on unreliable data to make it reliable) or drop them out completely - bad features could significantly decrease the quality of the model
Q: Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms?
A: ML algorithms are not magic sticks, they can't figure out anything unless you tell them what you are looking for. Can you describe what means 'unreliable'? If yes, you can, as I mentioned, use feature engineering or write a code which will fix the data. Otherwise no ML algorithm will be able to help you, without the description of what exactly you want to achieve
Q: Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
A: I don't think so just because the question itself is too open-ended. What means 'the quality of the data'?
Generally, here are couple of things for you to consider:
1) Spend some time on googling feature engineering guides. They cover how to prepare your data for you ML algorithms, refine it, fix it. Good data with good features dramatically increase the results.
2) You don't need to use all of features from original data. Some of features of original dataset are meaningless and you don't need to use them. Try to run gradient boosting machine or random forest classifier from scikit-learn on your dataset to perform classification (or regression, if you do regression). These algorithms also evaluate importance of each feature of original dataset. Part of your features will have extremely low importance for classification, so you may wish to drop them out completely or try to combine unimportant features together somehow to produce something more important.
Can anyone advise me way to build effective face classifier that may be able to classify many different faces (~1000)?
And i have only 1-5 examples of each face
I know about opencv face classifier, but it works bad for my task (many classes, a few samples).
It works alright for one face classification with small number of samples. But i think that 1k separate classifier is not good idea
I read a few articles about face recognition but methods from these articles reqiues a lot of samples of each class for work
PS Sorry for my writing mistakes. English in not my native language.
Actually, for giving you a proper answer, I'd be happy to know some details of your task and your data. Face Recognition is a non-trivial problem and there is no general solution for all sorts of image acquisition.
First of all, you should define how many sources of variation (posing, emotions, illumination, occlusions or time-lapse) you have in your sample and testing sets. Then you should choose an appropriate algorithm and, very importantly, preprocessing steps according to the types.
If you don't have any significant variations, then it is a good idea to consider for a small training set one of the Discrete Orthogonal Moments as a feature extraction method. They have a very strong ability to extract features without redundancy. Some of them (Hahn, Racah moments) can also work in two modes - local and global feature extraction. The topic is relatively new, and there are still few articles about it. Although, they are thought to become a very powerful tool in Image Recognition. They can be computed in near real-time by using recurrence relationships. For more information, have a look here and here.
If the pose of the individuals significantly varies, you may try to perform firstly pose correction by Active Appearance Model.
If there are lots of occlusions (glasses, hats) then using one of the local feature extractors may help.
If there is a significant time lapse between train and probe images, the local features of the faces could change over the age, then it's a good option to try one of the algorithms which use graphs for face representation so as to keep the face topology.
I believe that non of the above are implemented in OpenCV, but for some of them you can find MATLAB implementation.
I'm not native speaker as well, so sorry for the grammar
Coming to your problem , it is very unique in its way. As you said there are only few images per class , the model which we train should either have an awesome architecture which can create better features within an image itself , or there should be an different approach which can achieve this task .
I have four things which I can share as of now :
Do data pre-processing and then create a bigger dataset and train on a neural network ideally. Here, we can do pre-processing like:
- image rotation
- image shearing
- image scaling
- image blurring
- image stretching
- image translation
and create atleast 200 images per class. Please checkout opencv documentation which provides many more methods on how you can increase the size of your dataset. Once you do this, then we can apply transfer learning , which is a better approach than training a neural network from scratch.
Transfer learning is a method where we train a network on our own custom classes , and this network is already pre-trained on 1000's of classes. Since our data here is very less, I would prefer transfer learning only. I have written a blog on how you can approach this using tranfer learning after you have the required amount of data. It is linked here. Face recognition also is a classification task itself, where each human is a separate class. So, follow the instructions given in the blog , may be it would help you create your own powerful classifer.
Another suggestion would be , after creating a dataset , encode them properly. This encoding would help you preserve the features in an image and can help you train better networks. VLAD ,Fisher , Bag of Words are few encoding techniques. You can search few repositories online which have implemented these already on ORL database. Once you encode , train the network on the encodings , you will obviously see a better performance.
Even do check out , Siamese network here which is meant for this purpose I feel . Here they compare two images with similar characteristics on different networks and there by achieve better classification accuracies . Git repository is here.
Another standard approach would be using SVM , Random forests since the data is less. If you still prefer neural networks the above methods would serve you the purpose. If you intend to go with encodings , then I would suggest random forests , as it is highly preferrable in learning and flexible too.
Hopefully , this answer would help you proceed in the right direction of achieving things.
You might want to take a look at OpenFace, a Python and Torch implementantion of face recognition with deep neural networks: https://cmusatyalab.github.io/openface/
This is a research question not a direct programming question.
I am working on a symbol recognition algorithm, What the software currently does, it takes an image, divide it into contours (blobs) and start matching each contour with a list of predefined templates. Then for each contour it takes the one that has the highest match rate.
The algorithm is doing fairely however I need to train it better. What I mean is this:
I want to use a machine learning algorithm that will train the algorithm to have better matching. So lets take an example:
I run the recognition on a symbol, the algorithm will run and find that this symbol is a car, then I have to confirm that result (maybe by clicking on "Yes" or "No") the algorithm should learn from that. So if I click on NO the algorithm should learn that this is not a car and will have better result next time (maybe try to match something else). while if i click on YES he will know that he was correct and next time he will perform better when searching for a car.
This is the concept I am trying to research. I need documents or algorithm that can achieve this sort of things. I am not looking for implementations or programming, just concept or researches.
I have done many researches and read a lot about machine learning, neural networks, decision trees.... but i was not able to know how can I use any in my scenarion.
I hope I was clear and this type of question is allowed on stack overflow. if not I'm sorry
Thanks a lot for any help or tip
Image recognition is still a challenge in the community. What you described in your process of manually clicking yes/no is just creating labeled data. Since this is a very broad area, I will just point you to a few links that might be useful.
To get start, you might want to use some existing image databases instead of creating your own, which saves you a lot of effort. e.g., this car dataset in UCIC image db.
Since you already have the background of machine learning, you can take a look at some survey paper that exactly match your project interests, e.g., search object recognition survey paper or feature extraction car in google.
Then you can dive into some good papers and see whether they are suitable for your project. For example, you can check the two papers below that linked with the UCIC image db.
Shivani Agarwal, Aatif Awan, and Dan Roth,
Learning to detect objects in images via a sparse, part-based representation.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1475-1490, 2004.
Shivani Agarwal and Dan Roth,
Learning a sparse representation for object detection.
In Proceedings of the Seventh European Conference on Computer Vision, Part IV, pages 113-130, Copenhagen, Denmark, 2002.
Also check for some implemented softwares instead of starting from scratch, in your case, opencv should be good one to start with.
For image recognition, feature extraction is one of the most important step. You might want to check some stat-of-the-art algorithms in the community. (SIFT, mean-shift, harr features etc).
Boosting algorithm might also be useful when you reach the classification step. I see a lot of scholars mention this in image recognition community.
As #nickbar suggest, discuss more at https://stats.stackexchange.com/