'Nearest neighbor classification' diaporamas de présentation

Learning Embeddings for Similarity-Based Retrieval

Learning Embeddings for Similarity-Based Retrieval. Vassilis Athitsos Computer Science Department Boston University. Overview. Background on similarity-based retrieval and embeddings. BoostMap. Embedding optimization using machine learning. Query-sensitive embeddings.

1.12k views • 98 slides

Learning Embeddings for Similarity-Based Retrieval

Learning Embeddings for Similarity-Based Retrieval. Vassilis Athitsos Computer Science Department Boston University. Overview. Background on similarity-based retrieval and embeddings. BoostMap. Embedding optimization using machine learning. Query-sensitive embeddings.

1.17k views • 98 slides

Learning Embeddings for Similarity-Based Retrieval

Learning Embeddings for Similarity-Based Retrieval. Vassilis Athitsos Computer Science Department Boston University. Overview. Background on similarity-based retrieval and embeddings. BoostMap. Embedding optimization using machine learning. Query-sensitive embeddings.

1.08k views • 92 slides

Fall 2004, CIS, Temple University CIS527: Data Warehousing, Filtering, and Mining Lecture 8 Alternative Classification A

Fall 2004, CIS, Temple University CIS527: Data Warehousing, Filtering, and Mining Lecture 8 Alternative Classification Algorithms Lecture slides taken/modified from: Vipin Kumar ( http://www-users.cs.umn.edu/~kumar/csci5980/index.html ). Rule-Based Classifier.

783 views • 66 slides

A Survey on Text Classification

A Survey on Text Classification. December 10, 2003 20033077 Dongho Kim KAIST. Contents. Introduction Statistical Properties of Text Feature Selection Feature Space Reduction Classification Methods Using SVM and TSVM Hierarchical Text Classification Summary. Introduction.

879 views • 50 slides

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan

535 views • 46 slides

Modified from Stanford CS276 slides Chap. 14: Text Classification; Vector space classification

598 views • 46 slides

FEATURE EXTRACTION AND FUSION TECHNIQUES FOR PATCH-BASED FACE RECOGITION

FEATURE EXTRACTION AND FUSION TECHNIQUES FOR PATCH-BASED FACE RECOGITION. Berkay Topcu Sabanc? University, 2009. Out line. Introduction Feature Extraction Dimensionality Reduction Normalization Methods Patch-Based Face Recognition Patch-Based Methods

715 views • 46 slides

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan

468 views • 46 slides

No quiz this week.

No quiz this week. . Reading. Principal components analysis: Textbook, Chapter 10, Section 10.2 Smith, A Tutorial on Principal Components Analysis (linked to class webpage) Evolutionary Learning Textbook, Chapter 12. Dimensionality Reduction: Principal Components Analysis.

528 views • 43 slides

Lecture 14: Text Classification; Vector space classification

631 views • 43 slides

INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID

INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID. Lecture # 43 Classification. ACKNOWLEDGEMENTS. The presentation of this lecture has been taken from the following sources

514 views • 23 slides

document

pTrees p redicate Tree technologie s provide fast, accurate horizontal processing of compressed, data-mining-ready, vertical data structures. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. course. 2. 3. 4. 5. PINE Podium Incremental Neighborhood Evaluator

266 views • 19 slides

Nonparametric Methods: Nearest Neighbors

Nonparametric Methods: Nearest Neighbors. Oliver Schulte Machine Learning 726. Instance-based Methods. Model-based methods: estimate a fixed set of model parameters from data. compute prediction in closed form using parameters. Instance-based methods:

465 views • 17 slides

Classification Nearest Neighbor

Classification Nearest Neighbor. Instance based classifiers. Store the training samples Use training samples to predict the class label of unseen samples. Instance based classifiers. Examples: Rote learner memorize entire training data

312 views • 17 slides

Welcome! MSCIT 521: Knowledge Discovery and Data Mining

Welcome! MSCIT 521: Knowledge Discovery and Data Mining. Qiang Yang Hong Kong University of Science and Technology qyang@cs.ust.hk http://www.cs.ust.hk. KDDCUP from past years 2007: Predict if a user is going to rate a movie? Predict how many users are going to rate a movie? 2006:

559 views • 15 slides

Time Series Bitmap Experiments

Time Series Bitmap Experiments. This file contains full color, large scale versions of the experiments shown in the paper, and additional experiments which were omitted because of space constraints Note that in every case, all the data is freely available.

269 views • 12 slides

M.S. Student, Daewon Ko 07. 22. 2014

Introduction to LMNN and progress review . M.S. Student, Daewon Ko 07. 22. 2014. Contents. Definition of LMNN Schematic illustration of LMNN Algorithm of LMNN Current progress review of my research. Definition of LMNN. Large margin nearest neighbor(LMNN) classification

217 views • 10 slides

Nearest-Neighbor Classifiers

K nearest neighbor classification Presented by Vipin Kumar University of Minnesota kumar@cs.umn.edu Based on discussion in "Intro to Data Mining" by Tan, Steinbach, Kumar. Nearest-Neighbor Classifiers. Requires three things The set of stored records

751 views • 8 slides

Optimized Nearest Neighbor Methods

Cam Weighted Distance vs. Statistical Confidence. Optimized Nearest Neighbor Methods. Robert R. Puckett. Cam-Weighted Distance. Deforms the distribution by transformation Simulates strengthening and weakening effects between prototypes.

190 views • 6 slides