Blog
The latest from Google Research
And the award goes to...
Wednesday, March 18, 2009
Posted by Fernando Pereira, Research Director
Corinna Cortes, Head of Google Research in New York, has just been awarded the ACM Paris Kanellakis Theory and Practice Award jointly with Vladimir Vapnik (Royal Holloway College and NEC Research). The award recognizes their invention in the early 1990s of the
soft-margin support vector machine
, which has become the supervised machine learning method of choice for applications ranging from image analysis to document classification to bioinformatics.
What is so important about this invention? In supervised machine learning, we create algorithms that can learn a rule to accurately classify new examples based on a set of training examples (e.g. spam or non-spam). There is no single attribute of an email message that tells us with certainty that it is spam. Instead, many attributes have to be considered, forming a vector of very high dimension. The same situation arises in many other machine practical learning tasks, including many that we work on at Google.
To learn accurate classifiers, we need to solve several big problems. First, the rule learned from the training data should be accurate on new test examples, even though it has not seen those examples. In other words, the rule must generalize well. Second, we must be able to find the optimal rule efficiently. Both of these problems are especially daunting for very high dimensional data. Third, the method for computing the rule should be able to accommodate errors in the training data, such as messages that are given conflicting labels by different people (my spam may be your ham).
Soft-margin support vector machines wrap these three problems together into an elegant mathematical package. The crucial insight is that classification problems of this kind can be expressed as finding in very high dimension (or even infinite dimension) the hyperplane that best separates the positive examples (ham) from the negative ones (spam).
Remarkably, the solution of this problem does not depend on the dimensionality of the data, it depends only on the pairwise similarities between the training examples determined by the agreement or disagreement between corresponding attributes. Furthermore, a hyperplane that separates the training data well can be shown to generalize well to unseen data with the same statistical properties.
Now, you might be asking how could this be done if the training data is inconsistently labeled. After all, you cannot have the same example on both sides of the separating hyperplane. That's where the soft margin idea comes in: the quadratic optimization program that finds the optimal separating hyperplane can be cleverly modified to "give up" on a fraction of the training examples that cannot be classified correctly.
With this crucial improvement, support vector machines became really practical, while the core ideas have had huge influence in the development of further learning algorithms for an ever wider range of tasks.
Congratulations to Corinna (and Vladimir) on the well-deserved award.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2022
May
Apr
Mar
Feb
Jan
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
