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what does the output vector of a word in word2vec represent?
Fixed width contexts for each word are used as input into a neural network. The output of the network is a vector of float values - aka the word embedding - of a given dimension (typically 50 or 100). The network is trained so as to provide good word embedding given the train/test corpus. One can easily come up with a fixed size input for any word - say M words to the left and N words to the righ

Categories : Machine Learning

Semantic Relations finding algorithms
Your answer is not really clear but I can give you some details about the main approaches in Relation Extraction (RE) task: Supervised RE has mostly employed kernel-based approaches : see this link Distant supervision was introduced in bioinformatics : the main idea of this approach is to create its own training data by heuristically matching the contents of relation repositories to correspondi

Categories : Machine Learning

mahout for content based recomendation
In mahout v1 from here https://github.com/apache/mahout your can use "spark-rowsimilarity" to create indicators for each type of metadata, categroy, cost, and ingredients. This will give you three matrices containing similar items for each item based on that particular metadata. This will give you a "more like this" type of recommendation. You can also try combining the metadata into one input mat

Categories : Machine Learning

Reinforcement learning algorithms for continuous states, discrete actions
Applying Q-learning in continuous (states and/or actions) spaces is not a trivial task. This is especially true when trying to combine Q-learning with a global function approximator such as a NN (I understand that you refer to the common multilayer perceptron and the backpropagation algorithm). You can read more in the Rich Sutton's page. A better (or at least more easy) solution is to use local a

Categories : Machine Learning

Training ML classifier for a group of users
There are a lot of different ways to determine what's "interesting." I think reddit has a pretty good model to look at in considering different options. They have different categories, like "hot", or "controversial", etc. So a couple options depending on what you/your professor want: Take the net number of likes (like = +1, dislike = -1) Take just the number of likes Take the total number of ra

Categories : Machine Learning

How do I balance a training dataset which has very high number of samples for a certain class?
You should not balance the dataset, you should train a classifier in a balanced manner. Nearly all existing classifiers can be trained with some cost sensitive objective. For example - SVMs let you "weight" your samples, simply weight samples of the smaller class more. Similarly Naive Bayes has classes priors - change them! Random forest, Neural networks, Logistic regression, they all let you some

Categories : Machine Learning


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Spiking Neural Network Classifier Implementation
what does the output vector of a word in word2vec represent?
Semantic Relations finding algorithms
mahout for content based recomendation
Reinforcement learning algorithms for continuous states, discrete actions
Training ML classifier for a group of users
How do I balance a training dataset which has very high number of samples for a certain class?
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