Pentagon-funded research aims to predict when crimes are gang-related

Pentagon-funded research aims to predict when crimes are gang-related

  • April 27, 2018
Table of Contents

Pentagon-funded research aims to predict when crimes are gang-related

The paper attempts to predict whether crimes are gang-related using a neural network, a complex computational system modeled after a human brain that “learns” to classify or identify items based on ingesting a training dataset. The authors selected what they determined to be the four most important features (number of suspects, primary weapon used, the type of premises where the crime took place, and the narrative description of the crime) for identifying a gang-related crime from 2014–16 LAPD data and cross-referenced the crime incidents with a 2009 LAPD map of gang territory to create a training dataset for their neural network.

Source: theverge.com

Tags :
Share :
comments powered by Disqus

Related Posts

TDM: From Model-Free to Model-Based Deep Reinforcement Learning

TDM: From Model-Free to Model-Based Deep Reinforcement Learning

While simple, this thought experiment highlights some important aspects of human intelligence. For some tasks, we use a trial-and-error approach, and for others we use a planning approach. A similar phenomena seems to have emerged in reinforcement learning (RL).

Read More
Lessons from My First Two Years of AI Research

Lessons from My First Two Years of AI Research

A friend of mine who is about to start a career in artificial intelligence research recently asked what I wish I had known when I started two years ago. Below are some lessons I have learned so far. They range from general life lessons to relatively specific tricks of the AI trade.

Read More
Introduction to Decision Tree Learning

Introduction to Decision Tree Learning

From Kaggle to classrooms, one of the first lessons in machine learning involves decision trees. The reason for the focus on decision trees is that they aren’t very mathematics heavy compared to other ML approaches, and at the same time, they provide reasonable accuracy on classification problems.

Read More