From playlist k-Nearest Neighbor Algorithm
k-NN 4: which distance function?
[http://bit.ly/k-NN] The nearest-neighbour algorithm is sensitive to the choice of distance function. Euclidean distance (L2) is a common choice, but it may lead to sub-optimal performance. We discuss Minkowski (p-norm) distance functions, which generalise the Euclidean distance, and can a
From playlist Nearest Neighbour Methods
How to Find the Probability Distribution for the Sample Proportion
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How to Find the Probability Distribution for the Sample Proportion
From playlist Statistics
k nearest neighbor (kNN): how it works
[http://bit.ly/k-NN] The k-nearest neighbor (k-NN) algorithm is based on the intuition that similar instances should have similar class labels (in classification) or similar target values (regression). The algorithm is very simple, but is capable of learning highly-complex non-linear decis
From playlist Nearest Neighbour Methods
The Normal Distribution (1 of 3: Introductory definition)
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From playlist The Normal Distribution
Want to learn Data Science? Announcing a New Course from Stellebosch University
#DataScience #Python #Course #Stellenbosch #University #Google #Colab The signup link is here: http://www.sun.ac.za/english/data-science-and-computational-thinking/Pages/Introduction-to-Data-Science-and-Computational-Thinking.aspx This is an announcement about a new course in Data Scie
From playlist Data Science @ Stellenbosch University
Intrinsic and extrinsic geometries of correlated many-body states by S. R. Hassan
DISCUSSION MEETING NOVEL PHASES OF QUANTUM MATTER ORGANIZERS: Adhip Agarwala, Sumilan Banerjee, Subhro Bhattacharjee, Abhishodh Prakash and Smitha Vishveshwara DATE: 23 December 2019 to 02 January 2020 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Recent theoretical and experimental
From playlist Novel Phases of Quantum Matter 2019
Sculpting tissues, building organs by Maithreyi Narasimha
Program ICTP - ICTS Winter School on Quantitative Systems Biology ORGANIZERS: Buzz Baum, Guillaume Salbreux, Stefano Di Talia and Vijaykumar Krishnamurthy DATE: 03 December 2019 to 20 December 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore The development of an organism from a s
From playlist Winter School on Quantitative Systems Biology: Quantitative Approaches in Ecosystem Ecology 2020
k-NN 2: classification and regression
[http://bit.ly/k-NN] The k-NN algorithm operates as follows. For a new test instance, we first compute its distance to all the N training instances, and keep a small number k of nearest neighbours. For classification, we then predict the most dominant class among the k neighbours. For regr
From playlist Nearest Neighbour Methods
Random Matrix Theory and its Applications by Satya Majumdar ( Lecture 4 )
PROGRAM BANGALORE SCHOOL ON STATISTICAL PHYSICS - X ORGANIZERS : Abhishek Dhar and Sanjib Sabhapandit DATE : 17 June 2019 to 28 June 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the tenth in the series. This is a pedagogical school, aimed at bridgin
From playlist Bangalore School on Statistical Physics - X (2019)
Nearest neighbor (2): k-nearest neighbor
Basic k-nearest neighbor algorithm for classification and regression
From playlist cs273a
Nexus trimester - Yitong Yin (Nanjing University)
Rectangle inequalities for data structure lower bounds Yitong Yin (Nanjing University) February 23, 2016 Abstract: The richness lemma is a classic rectangle-based technique for asymmetric communication complexity and cell-probe lower bounds. The technique was enhanced by the Patrascu-Thoru
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
AQA Decision 1 8.04 The Travelling Salesperson Problem: The Nearest Neighbour Algorithm
I work through an example of the Nearest Neighbour Algorithm using a matrix and go through what to look out for.
From playlist [OLD SPEC] TEACHING AQA DECISION 1 (D1)
k-NN 5: resolving ties and missing values
[http://bit.ly/k-NN] For k greater than 1 we can get ties (equal number of positive and negative examples) in the k nearest neighbours. We discuss three different strategies for breaking ties (random, prior, and 1-NN). We also discuss the need to impute (fill-in) any missing values in our
From playlist Nearest Neighbour Methods
AQA Decision 1 8.06a The Travelling Salesperson Problem ex2: Nearest Neighbour Algorithm
I work through a second example of using the Nearest Neighbour Algorithm, this time without writing anything on the matrix.
From playlist [OLD SPEC] TEACHING AQA DECISION 1 (D1)
[http://bit.ly/k-NN] Inverted index is a data structure appropriate for high-dimensional discrete data. It allows us to find nearest neighbours faster by using than direct comparisons. It is an exact technique (not approximate), but only works for highly-sparse discrete attributes (e.g. te
From playlist Nearest Neighbour Methods