I want to be able to use some sort of Machine Learning or similar to generalize from lots of sets of items each with a label, lets call it a box. For each box their is contained inside a set of items with an id and string identifier. I want to as simply and as fast as possible make a generalization about what items might be found in each box. I then want to be able to lookup the label of the box and get a list of the items inside from the box's name. I am using C# and would like to learn to write this myself however there are so many different types of ML and I am not sure if there is a simpler solution? there could be up to 100 different boxes and sometimes unique boxes. The items inside the boxes could number more like 200-300 different items.
Asked
Active
Viewed 101 times
0
-
So from my understanding, you want to group items into lists of objects, with each list retrieved based off a string index as well as a number index? Or would the string index alone sufice? If so, you may just be looking at a Dictionary of Dictionaries. – Kiradien Jun 16 '17 at 00:46
-
Sorry, If I mislead you, no I want the algorithm to generalize about what items might be in the box with a particular label and then create a general list from the names. It think a decision tree might be close or what I need. However I am inexperienced with ML software and coding and so I wanted to know if something simpler that can still generalize exists rather than over-think or complicate what I need to do. – user7773578 Jun 16 '17 at 06:28