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AI: a super general term, means a bit of everything... and nothing at the same time. It's all about building intelligent machines, even though its meaning is not fully developed. It's not used in a rigorous way at all. In fact, scientists prefer to use more technical terms like the others you listed. ML: it's an approach to data, and it's all based on ...


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Artificial Intelligence It's a general idea to attain human-level intelligence using machines. This is more of a pursuit of human-kind i.e. an ongoing journey. It goes back to 1950 when Alan Turing devised the Idea of the Turing test. Machine Learning This is an approach where the program(model) is automatically built using the data. This approach is unlike ...


2

If you have two images, you first start to make histograms of the values (0-255) in the three color channels (red, green, and blue). In the article 25 bins are used, meaning that the values are assigned to one of 25 ranges. The second step is to then concatenate the the three single channel histograms to one histogram for the full image. Since each image ...


2

First of all, no one knows how google search works except what google officially publishes. But I give you a simple algorithm for query correction (I have implemented this previously in production). It is a simple prediction based on spell-check. First you detect the (possible) typo. Then you find the query which maximizes the likelihood. This search is done ...


1

There are many well-documented techniques to help you out with this. Collaborative filtering and even nearest neighbour search can help (given you have created good embeddings for the products using neural networks with multimodal input). You would initially want to sort the list by frequency then date. Once, you have that you need to find related items to ...


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This question is quite open, but nonetheless, here are some: lemmatization/stemming only makes sense in languages where there is a lemma/stem in the word. Some languages like Chinese have no morphological variations (apart from some arguable cases like the explicit plural 们), and therefore lemmatization and stemming are not applied in Chinese. Word-based ...


1

That is indeed a drawback with grid search strategy, since you must know in advance each one of the possible combinations to try out, and that might be not optimal neither to get the best evaluation metric value nor in computation performance. You have other interesting strategies, not exhaustive hyperparameter search, for instance random search or based on ...


1

Machine learning is the field that researches methods that fit (= optimize) model structures to data and output a final model that is based on the combination of model structure and optimized model parameters. Statistics and ML are intersecting fields such that some methods belong to both fields, however there exist some methods that only belong to ML and ...


1

Machine Learning (ML) is a subset of Artificial Intelligence (AI). For example, the Minimax algorithm is also part of the larger field of AI, but the approach is not based on ML. In fact these algorithms showed much more promising results in the earlier stages of AI research (Deep Blue was the first computer to win a grandmaster in chess and it did not use ...


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There are a couple options: Up-sample your data (as you described). Use SMOTE or a similar method to up-sample the lesser class to achieve closer to 50/50 split on positive/negative class respectively If you have a lot of data - down-sample your more frequent class (thereby throwing away a lot of examples in the negative class) Select performance metrics ...


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I think this paper just compares algorithms: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.9414&rep=rep1&type=pdf If you want something specific, here's the white paper for SLIPPER: https://www.aaai.org/Papers/AAAI/1999/AAAI99-049.pdf Hope that helps.


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One option is fingerprinting. If two objects have the same fingerprint, they are probably the same object. Depending the technique used, the fingerprint can not tell about approximate duplicates.


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The default number of estimators is 100. Reducing the number of estimators may work.


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I guess you are refering to "similarity-based clustering", which is Clustering, which only uses the similarities between objects but does not require to represent the objects via feature vectors, is called similarity-based clustering. There are 3 approaches: Aspect model [... ]Hofmann and Puzicha [1999], Hofmann et al. [1999], considers ...


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