'Dimensional spaces' presentation slideshows

Support vector machine

867 views • 65 slides

Machine Learning and Linear Algebra of Large Informatics Graphs

1.25k views • 52 slides

On a Theory of Similarity functions for Learning and Clustering

On a Theory of Similarity functions for Learning and Clustering. Avrim Blum Carnegie Mellon University This talk is based on work joint with Nina Balcan, Nati Srebro and Santosh Vempala. Theory and Practice of Computational Learning, 2009. 2-minute version.

573 views • 44 slides

Lecture outline

Lecture outline. Density-based clustering (DB-Scan) Reference: Martin Ester, Hans-Peter Kriegel , Jorg Sander, Xiaowei Xu : A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 2006 Co-clustering (or bi-clustering) References:

538 views • 37 slides

Learning Models of Relational Stochastic Processes

Learning Models of Relational Stochastic Processes. Sumit Sanghai. P2. P1. P3. V1. A1. A2. Motivation. Features of real-world domains Multiple classes, objects, relations. P2. ?. P1. P3. V1. A1. A2. ?. ?. Motivation. Features of real-world domains

518 views • 36 slides

Clustering Preliminaries

Clustering Preliminaries. Applications Euclidean/Non-Euclidean Spaces Distance Measures. The Problem of Clustering.

580 views • 34 slides

Clustering

615 views • 33 slides

Drawing and Painting 4-22 to 4-25 Livoti

439 views • 30 slides

Clustering Preliminaries

Clustering Preliminaries. Applications Euclidean/Non-Euclidean Spaces Distance Measures. The Problem of Clustering.

424 views • 30 slides

Geometric Shapes and Area

Geometric Shapes and Area. Shape. Shape describes the two-dimensional contour that characterizes an object or area, in contrast to a three-dimensional solid. Examples include:. Area.

461 views • 26 slides

375 views • 25 slides

Geometric Shapes and Area

Geometric Shapes and Area. Shape describes the two-dimensional contour that characterizes an object or area, in contrast to a three-dimensional solid. Examples include:. Area.

514 views • 25 slides

From last time

285 views • 22 slides

An Intelligent & Incremental Approach to kNN using R-trees

An Intelligent & Incremental Approach to kNN using R-trees. DJ Oneil & Esten Rye (G01). Presentation Outline. Motivation Related Work Problem Definition Approach Validation Conclusion. Motivation. kNN is a popular (GIS, AI, Pattern Recognition, Clustering, Outlier Detection)

279 views • 17 slides

The Coverage Problem in Wireless Ad Hoc Sensor Networks

The Coverage Problem in Wireless Ad Hoc Sensor Networks. Supervisor: Prof. Sanjay Srivastava By, Rucha Kulkarni 200811039. Outline. Introduction Types of coverage. Computational Geometry Background Study Algorithm for coverage calculation Open problems.

354 views • 14 slides

Particle Filtering

136 views • 12 slides

Particle Filtering

224 views • 12 slides