BuboFlash - helps with learning

Edited, memorised or added to reading queue

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

Annotation 7096129162508

#abm #agent-based #machine-learning #model #priority

Universal Framework for Agent based Models - 4 phases

(1) Initialization (2) Experience(3) Training(4) Application

In the Training phase, the Artificial Neural Network is trained to solve the following classification problem: The Neural Network is presented with the input of the agent and a decision and should estimate whether this is a good, a neutral or a bad decision. Various methods could be used for this type of problem. In order to keep the framework versatile, a hidden layer approach [49,50] is used here. After training the Artificial Neural Network, an unused part of the database gathered in phase two is used for cross validation [51]. The Artificial Neural Network is implemented using scikit-learn

status	not read	reprioritisations
last reprioritisation on		suggested re-reading day
started reading on		finished reading on

pdf

cannot see any pdfs

Flashcard 7697625910540

Tags

#feature-engineering #lstm #recurrent-neural-networks #rnn

Question

Since customer transactions occur sequentially, they can be modeled as a sequence prediction task using an RNN as well, where all firm actions and customer [...] are represented by elements in a vector.

Answer

responses

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

Parent (intermediate) annotation

Open it
Since customer transactions occur sequentially, they can be modeled as a sequence prediction task using an RNN as well, where all firm actions and customer responses are represented by elements in a vector.

Original toplevel document (pdf)