Collaborative Filtering: Using Machine Learning and Statistical Techniques - Image 1

£45.00

LAP Lambert Academic Publishing Collaborative Filtering: Using Machine Learning and Statistical Techniques

Name: Collaborative Filtering: Using Machine Learning and Statistical Techniques
Brand: LAP Lambert Academic Publishing
Price: 45.0 GBP
Availability: InStock

Computing & Internet

Price data last checked 15 day(s) ago - will refresh soon

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

It has never been this cheap. We have no record of a lower price.

£45 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 76 days · 76 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 76 days • 1 price levels

Days at Price

Price Analysis

Most common price: £45 (76 days, 100.0%)

Price range: £45 - £45

Price levels: 1 different prices over 76 days

Description

Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation).

Product Specifications

Brand: LAP Lambert Academic Publishing
Format: paperback
ASIN: 3659429090
Category: Books > Subjects > Computing & Internet
Domain: Amazon UK
Release Date: 10 July 2013
Listed Since: 11 July 2013

Barcode

No barcode data available

LAP Lambert Academic Publishing Collaborative Filtering: Using Machine Learning and Statistical Techniques

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Robust Latent Feature Learning for Incomplete Big Data (SpringerBriefs in Computer Science)

Embedding Privacy in Data Mining: Designing Algorithms with Better Privacy and Utility Tradeoffs

Machine Learning based Stochastic Techniques for Collaborative Privacy: A Case Study in Social Recommender Services

Data Mining, Knowledge Discovery in Databases:: Hybrid Anomaly Detection Approach

Content Clustering of Computer Mediated Courseware: Using Data Mining Technique

Trust-based Collective View Prediction

A New Trend of Knowledge Discovery Toward Intelligent Data Analysis: A New Trend of KDD Using DM and FCARB

Robust Random Regression Imputation for Missing Data: English

Estimation-Based Adaptive Filtering and Control: A systematic approach to adaptive filter design

Riemannian Geometry and Statistical Machine Learning

Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)

Heterogeneous Information Network Analysis and Applications (Data Analytics)

Image Compressive Sharing System Using Transform Coding: Practical application to compress and encrypt images

Exploration Of Soft Computing Approaches on Itemset Utility Mining

Fuel Efficiency (MPG) Prediction Using Machine Learning

Recommendation Systems in Software Engineering

Recommendation Systems in Software Engineering

Sequential Pattern Mining from Web Log Data: Concepts,Techniques and Applications of Web Usage Mining

Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning: 17 (Studies in Computational Intelligence, 17)

Linear Regression Models under Multicollinearity: Ridge Regression Inferential Techniques

Black book of Machine Learning and Deep Learning

Advances in Combining Intelligent Methods: Postproceedings of the 5th International Workshop CIMA-2015, Vietri sul Mare, Italy, November 2015 (at ... (Intelligent Systems Reference Library, 116)

Intelligent Predictive Maintenance Frameworks for Fault Classification: Hybridising Machine Learning Techniques

Clustering Based Unit Commitment Employing Soft Computing Techniques: Implementation Of Clustering Based Unit Commitment Employing Soft Computing Techniques