i am working on text classification in weka. I want to use a classifier from rapidminer. I just saw the "weka.jar" in rapidminer lib directory which may mean that we can use some cross functionality.
Can we use a classifier or functionality from rapid miner whereas some other functionalities from weka ???
I'm afraid you can't!!
Weka is not covering that functionality.
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I have an Excel file containing the following data.
I want to apply it on Weka by k-nearest neighbor classifier.
How I can make a prediction of the new instance?
How can I set the parameters of this instance to obtain prediction about it?
I don't think you have enough data to work with here. Your model will be wildly inaccurate. If you are starting with machine learning, I would recommend the Iris data set to start with. I started with machine learning here.
If you want to start with Weka, I would use a dataset from researchers, like the MNIST database of handwritten digits which can be found here, and a guide for it in python here. On the same site, there is a tutorial for the Weka gui, if you look hard enough.
I am working on my college project and for that I have to recognize different hand gestures, so can any one tell me that how can I learn this image recognization quickly using python?
If you're using Python, I think you would better use Tensorflow.
Check https://github.com/tensorflow/models/tree/master/object_detection.
It is easy to follow instructions and they provide convenient script for retraining a detection model.
If you need to train a model with custom data, you have to prepared images dataset annotated with bounding boxes.
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i am working on this project asssigned by university as final project. But the issue is i am not getting any help from the internet so i thought may be asking here can solve issue. i had read many articles but they had no code or guidance and i am confused what to do. Basically it is an image processing work with machine learning. Data set can be found easily but issue is python python learning algorithm and code
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I presume if it's your final project you have to create the program yourself rather than ripping it straight from the internet. If you want a good starting point which you can customise Tensor Flow from Google is very good. You'll want to understand how it works (i.e. how machine learning works) but as a first step there's a good example of image processing on the website in the form of number recognition (which is also the "Hello World" of machine learning).
https://www.tensorflow.org/get_started/mnist/beginners
This also provides a good intro to machine learning with neural nets: https://www.youtube.com/watch?v=uXt8qF2Zzfo
One note on Tensor Flow, you'll probably have to use Python 3.5+ as in my experience it can be difficult getting it on 2.7.
First of all I need to know what type of data are you using because depending on your data, if it is a MRI or PET scan or CT, there could be different suggestion for using machine learning in python for detection.
However, I suppose your main dataset consist of MR images, I am attaching an article which I found it a great overview of different methods>
This project compares four different machine learning algorithms: Decision Tree, Majority, Nearest Neighbors, and Best Z-Score (an algorithm of my own design that is a slight variant of the Na¨ıve Bayes algorithm)
https://users.soe.ucsc.edu/~karplus/abe/Science_Fair_2012_report.pdf
Here, breast cancer and colorectal cancer have been considered and the algorithms that performed best (Best Z-Score and Nearest Neighbors) used all features in classifying a sample. Decision Tree used only 13 features for classifying a sample and gave mediocre results. Majority did not look at any features and did worst. All algorithms except Decision Tree were fast to train and test. Decision Tree was slow, because it had to look at each feature in turn, calculating the information gain of every possible choice of cutpoint.
My Solution:-
Lung Image Database Consortium provides open access dataset for Lung Cancer Images.
Download it then apply any machine learning algorithm to classify images having tumor cells or not.
I attached a link for reference paper. They applied neural network to classify the images.
For coding part, use python "OpenCV" for image pre-processing and segmentation.
When it comes for classification part, use any machine learning libraries (tensorflow, keras, torch, scikit-learn... much more) as you are compatible to work with and perform classification using any better outperforming algorithms as you wish.
That's it..
Link for Reference Journal
So I am creating my own classifiers using the OpenCV Machine Learning module for age estimation. I can train my classifiers but the training takes a long time so I would like to see some output (status classifier, iterations done etc.). Is this possible? I'm using ml::Boost, ml::LogisticalRegression and ml::RTrees all inheriting cv::StatModel. Just to be clear i'm not using the given application for recognizing objects in images (opencv_createsamples and opencv_traincascade). The documentation is very limited so it's very hard to find something in it.
Thanks
Looks like there's an open feature request for a "progress bar" to provide some rudimentary feedback... See https://github.com/Itseez/opencv/issues/4881. Personally, I gave up on using the OpenCV ML a while back. There are several high-quality tools available to build machine learning models. I've personally used Google's Tensorflow, but I've heard good things about Theano and Caffe as well.
I've been searching the web on how to generate J48 decision trees but so far after almost a couple days I haven't found any result about how to generate a J48 decision without Weka, I mean manually by hand. The reason why I wanna do this is because I need to evaluate my data in an assignment.
I would appreciate any information about the j48 algorithm.
The J48 classifier implements the C4.5 algorithm. You should be able to use either a description of that or, if you need to be exactly like what Weka does, you can step through the code itself.
you can use weka as well for developing a simple code, what you have to do, download the jar file of weka, and study the API of weka which is provided by weka as well. and develop your own program to use the algorithm and implement it on your data