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Results tagged with machine-learning
Search options not deleted user 6550
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
1 vote
Technique/Algorithm for product categorization Machine Learning
I assume you are looking for a similarity measure between items. A quick and simple one is item-item cosine similarity. An item (product) can be represented by a vector $x$ with $x_i = 1$ if it was in …
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1 vote
Cosine similarity of averaged random word vectors
Nothing too surprising here. As you sample more and more words, the sample mean is a better and better estimator of the population mean. This is called the law of large numbers.
- 1,472
2 votes
Accepted
Why does this paper claim to have found a minimal width of $d_{in}+1$?
From the abstract of the paper: Specifically, we answer the following question: for a fixed $d_{in} \ge 1$, what is the minimal width $w$ so that neural nets with ReLU activations, input dimension $d …
- 1,472
2 votes
Difference between regret and pseudo-regret definitions in multi-armed bandits
After the formula for (1) on p. 60 (I found no relevant formula on p. 48) the linked version of the book says that the expectation is over both the learner's actions and the bandit randomness, so (1) …
- 1,472
1 vote
Struggling with Distribution Fitting in User Order Analysis – Need Advice!
Thanks for sharing your interview solutions notebook. I think your code is nice and clean, and you are using some nice tools like PyCaret which I didn't know about before. I've spotted a couple of pro …
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3 votes
Machine learning techniques for estimating users' age based on Facebook sites they like
Apart from the fancier methods you could try the Bayes formula $P(I | p_1 ... p_n) = {{P(p_1 ... p_n | I) P(I)} \over \sum_i (P(p_1 ... p_n | i) P(i))}$ $P(I | p_1 ... p_n)$ is the probability that …
- 1,472
3 votes
Recommender Model for Human Action in Income Protection
It looks like the inverse reinforcement learning problem defined by Stuart Russell as Given measurements of an agent’s behaviour over time, in a variety of circumstances. measurements of the sensory …
- 1,472
0 votes
Backpropagation with a different sized training set?
In modern libraries, such as tensorflow or pytorch, you can easily build custom loss functions and / or custom layers. For example, the last layer could be a frozen layer which computes the square len …
- 1,472
1 vote
Complete link clustering
I may have missed something in the definition in Wikipedia, but wouldn't the set {1,2,4,5,7,8} with $\Delta(x,y) = |x-y|$ be a counterexample? After the 4th step there are two clusters, {{1,2}, {4,5}} …
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1 vote
sklearn - overfitting problem
To see if SVM can capture any signal at all, try to balance your data: create training and test sets that consist of exactly 50% positive and 50% negative samples (i.e., by subsampling randomly from w …
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1 vote
Recommendations based on other products seen
For very large datasets there are more powerful methods than k-nn, try to look at what the well known companies like Google, Amazon, Netflix, etc. have published. Since you are learning, I assume you …
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2 votes
Why does softmax perform well on MNIST but poorly on EMNIST letters?
Maybe the EMNIST dataset is simply more difficult to classify. From EMNIST paper:
- 1,472
7 votes
How to correctly perform link prediction inference on a new, unseen graph?"
Based on your question and comments, this looks like a very interesting and challenging problem, quite a bit of time might be needed to understand what is possible or solve it. It seems that some meth …
- 1,472
2 votes
1 answer
229 views
Generic strategy for object detection
I have a huge collection of objects from which only a tiny fraction are in a class of interest. The collection is initially unlabelled, but labels can be added using an expensive operation (for exampl …
- 1,472
0 votes
How to predict an approximate weekly/monthly number, when the Unique Daily Visitors for that...
You can't retrospectively do it just with counts of unique visitors per day. If you represent the unique users on each day by sets $A_1, A_2, \dots, A_n$, the union can be as small as $|A_1|$, if all …
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