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I've been working on this project for a bit and would like to know which approach is the best out of the two that I have come up with. My project is about the weight of a potato.
Keep in mind it takes a lot of time and resources to record data.
Approach One
use a computer vision algorithm to find the height of an object. And since that height and weight have a linear relationship with a potato. We can use a linear regression model to figure out the weight of the object
Approach Two
collect a dataset of images of potatoes with their corresponding weights and preprocess the data using YOLO. Through this we can get the weight of the object.
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I am looking for the machine learning correct approach for predicting the lottery numbers, not the most accurate answer but at least we have some predicted output. I am implementing the regression based and neural network models for this. Is their any specific approach which follows this?
It is impossible. The lottery numbers are random - actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don't even try it.
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I want to implement a netwrok that know to distinguish between real and fake given images.
I don't want to use GAN bc it will be an overkill (training generator and discriminator and I already have the images).
What is the prefered framework to do this?
Does binary classifier is what I need?
Yes, binary classification sounds like a reasonable way to frame your problem.
GANs would be more suitable if you wanted to generate new images. In that case you could train a generator and a discriminator, and then use the former and discard the latter.
As I understand it, discriminator networks typically don't get used on their own (which appears to have been your line of thinking). The reason is that they become tightly coupled to the generator they've been trained with, and don't necessarily generalise beyond that.
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I want to train an output vector(which is from deep learning model) like fixed vector. Hence, I chose a cosine similarity between two vectors as the objective function. However, I don't know if that is a correct approach for my need.
No. The cosine similarity is a measure of how similar two items (samples in your dataset) are.
In contrast, the objective function when training a neural network should be a definition of the current estimation error over the data - so they are different things.
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I am having name and description of event and i want to find out about the categories of the event(for example is it entertainment event, politic event or something else).
I was searching on the web and i looked at some natural language processing techniques such as Latent Dirichlet Allocation but i can not see a way to use it in my situation.
Is it a good idea to try to categorize by having predefined keywords for each category, and then to query the text and decide by the amount of keywords from each category?
Can someone give me a clue about my problem ? Many thanks
One approach you could take is to start simple and use a bayesian classifier to analyze/classify your data.
I would approach this problem by taking your dataset and splitting it into a training dataset and a non-training dataset. Then, manually review each event and categorize it as a type of event. Using this training dataset to run your classifier against the remainder of your data.
This may not be ideal for a large amount of event types but it might be a way for you to get started addressing the problem.
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Hi i want to predict health level(High,medium,low) in leaf using image processing and data mining.So far i thought using extract colors from leaf using Bayes algorithm to predict healthy of leaf. and data mining part have completed now.but i need extra features for prediction.we only used orchid leaf.So i can't use vain structure.Can anyone help me to what are the other features can be extracted from leaf for identify health level of leaf.Any idea or comments help me to improve my project. Thanks
There are many possible approaches to a problem like this. One common method is the bag-of-features model. Take a look at this example using the Computer Vision System Toolbox in MATLAB.