Learning in Graphical Models

Main
Computers - Computer Science
Learning in Graphical Models

Learning in Graphical Models

Michael Irwin Jordan

0 / 0

คุณชอบหนังสือเล่มนี้มากแค่ไหน

คุณภาพของไฟล์เป็นอย่างไรบ้าง

ดาวน์โหลดหนังสือเพื่อประเมินคุณภาพของไฟล์

คุณภาพของไฟล์ที่คุณดาวน์โหลดมาเป็นอย่างไรบ้าง

Presents an exploration of issues related to learning within the graphical model formalism. This text covers topics such as inference for Bayesian networks; Monte Carlo methods; variational methods; and learning with Bayesian networks..

หมวดหมู่:

Computers - Computer Science

ปี:

1999

ฉบับพิมพ์ครั้งที่:

None

สำนักพิมพ์:

Independely Published

ภาษา:

english

จำนวนหน้า:

652

ISBN 10:

0262600323

ISBN 13:

9780262600323

ไฟล์:

PDF, 56.83 MB

IPFS:

,

english, 1999

อ่านออนไลน์

กำลังแปลงเป็น อยู่

การแปลงเป็น ล้มเหลว

คุณอาจสนใจ

Daphne Koller, Benjamin Packer — Instructor’s Manual for Probabilistic Graphical Models

Peter Leow — Genetic Algorithms Demystified

— ProgrammerLazy. SQL for Marketers: Dominate data analytics, data science, and big data. Data Science and Machine Learning in Python

Leonard Eddison [Eddison, Leonard] — Python Machine Learning: A Guide For Beginners (2nd Edition)

Richard E. Neapolitan — Learning Bayesian networks

Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani — Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

Steffen L. Lauritzen — Graphical models

Ethem Alpaydin — Introduction to Machine Learning

Masashi Sugiyama, Motoaki Kawanabe — Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Pierre Baldi, SÃ¸ren Brunak — Bioinformatics: the machine learning approach

Olivier Chapelle, Bernhard Scholkopf, Alexander Zien — Semi-Supervised Learning

Frey B.J. — Graphical Models for Machine Learning and Digital Communication

Clark Glymour — The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology

Peter D. Grünwald — The Minimum Description Length Principle (Adaptive Computation and Machine Learning)

Lise Getoor, Ben Taskar — Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

David J. Hand, Heikki Mannila, Padhraic Smyth — Principles of Data Mining

Ralf Herbrich — Learning Kernel Classifiers: Theory and Algorithms

Joaquin QuiÃ±onero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence — Dataset Shift in Machine Learning

Peter Spirtes, Clark Glymour, Richard Scheines — Causation, Prediction, and Search, Second Edition (Adaptive Computation and Machine Learning)

edited by Michael I. Jordan and Terrence J. Sejnowski. — Graphical models : foundations of neural computation

David Edwards — Introduction to Graphical Modelling 2nd edition

Dey, Nilanjan; Mahalle, Parikshit N.; Pathan, Mohd. Shafi; Wagh, Sanjeev — Applied machine learning for smart data analysis

Drton, Mathias; Lauritzen, Steffen L.; Maathuis, Marloes; Wainwright, Martin J — Handbook of graphical models

Olivier Pourret, Patrick Naïm, Bruce Marcot — Bayesian Networks: A Practical Guide to Applications (Statistics in Practice)

Christian Borgelt, Matthias Steinbrecher, Professor Dr Rudolf R Kruse — Graphical models: Representations for learning, reasoning and data mining

Yang Xiang — Probabilistic Reasoning in Multiagent Systems : A Graphical Models Approach

Rajeev Motwani, Prabhakar Raghavan — Randomized Algorithms

David Barber — Bayesian reasoning and machine learning

Jennifer S. Larson — Who's Buying? Who's Selling?

Barton Cobert, Josiane Cobert — 100 Questions & Answers About Your Child's Obesity

S. Y. Kung — Kernel Methods and Machine Learning

Michael Mitzenmacher, Eli Upfal — Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis

Roman Vershynin — High-Dimensional Probability

H. M. Schwartz — Multi-Agent Machine Learning: A Reinforcement Approach

Nitin Indurkhya, Fred J. Damerau (Editors) — Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series)

Robi Polikar (auth.), Cha Zhang, Yunqian Ma (eds.) — Ensemble Machine Learning: Methods and Applications

Gabriel Zachmann Elmar Langetepe — Geometric data structures for computer graphics

Ankush Mittal, Ankush Mittal; Ashraf Kassim — Bayesian Network Technologies: Applications and Graphical Models

Zhiyuan Chen, Bing Liu — Lifelong Machine Learning

Kiran R Karkera — Building Probabilistic Graphical Models with Python

Ankur Ankan, Abinash Panda — Mastering Probabilistic Graphical Models Using Python: Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

Adolf Galland — The First and the Last

คำที่ถูกค้นหาบ่อยที่สุด

บุ๊กลิสต์ที่เกี่ยวข้อง