Precision is the fraction of relevant instances among the retrieved instances. It’s asking the question: How many selected items are relevant? Recall is the fraction of relevant instances that have been retrieved over total relevant instances. It’s asking the question: How many relevant items are selected? Recall and Precision are inversely related. Example: On a… Continue reading Precision and Recall
What not is happening online? Nothing, probably. Possibly. Digitized books to a friend’s wedding, if not physical presence, online happens to be a promising solution. It is really hard to think of an activity not associated to the web lately. World Wide Web puts more than expected things together making them available at hands reach.… Continue reading The Notice from Web
After the first version release of my research work which was named as Knit Engine [Ver. 0.1], I decided to make a monthly release to keep up and monitor the progress of the work. They will be small releases to track the work, however nothing made public as of yet. This release Version 0.2, saw… Continue reading Knit Engine [Ver. 0.2]
It’s kind of a Notice. Like a note given to web and web enthusiasts. The book presents a very brief history of evolution and as well predicts a few things that could deliver an obligatory friendly web in the future. The book is divided into two parts. ‘Part A: The Evolution’ presents the history and… Continue reading The Web Circular – Ebook
This GitHub Repository contains all the programs from the ebook “Structures and C” The following can be found in the repository: Programs used in each of the chapters named with Bogie number Exercise programs and examples MCQ solutions The Book can be downloaded HERE.
It is the distance between two points present in the Euclidean space (In geometry, Euclidean space is referred as 2 dimensional Euclidean plane) Plane and Point Formula: The Euclidean distance between points p and q is the length of the line segment connecting them. In the Euclidean plane, if p = (p1, p2) and q… Continue reading Euclidean Distance
Leacock and Chodorow propose the below similarity measure: -log (length / (2 * D)) where: length is the length of the shortest path between the two concepts D is the maximum depth of the taxonomy It is a lexical semantic similarity measure.