Yahoo! Inc.

International Business Machines Corporation

Yahoo Assets LLC

Oath Inc.

Excalibur Ip, LLC

Kilian Quirin Weinberger

Malcolm G Slaney

Gerald James Tesauro

Rajarshi Das

Anirban Dasgupta

John C Langford

Yehuda Arie Koren

Sriram Jayaprakash Sathish

Kaushal Kurapati

Marc Aurelio Ranzato

Polly Ng

Eva Hoerster

A method of generating congruous metadata is provided. The method includes receiving a similarity measure between at least two multimedia objects. Each multimedia object has associated metadata. If the at least two multimedia objects are similar based on the similarity measure and a similarity threshold, the associated metadata of each of the multimedia objects are compared. Then, based on the comparison of the associated metadata of each of the at least two multimedia objects, the method further includes generating congruous metadata. Metadata may be tags, for example.

Generating congruous metadata for multimedia

In accordance with one aspect, methods and apparatus facilitate the filtering of unsolicited bulk electronic mail (email) sent from spammers. A plurality of recipient patterns for a plurality of emails from known spammers is logged. A plurality of recipient patterns for a plurality of emails from known non-spammers is also logged. A probabilistic model for predicting whether an unknown sender identity is a spammer is generated or modified based on the logged recipient patterns for the emails from known spammers and known non-spammers.

Apparatus and methods for classifying senders of unsolicited bulk emails

A system and method for improved classification. A first classifier is trained using a first process running on at least one computing device using a first set of training images relating to a class of images. A set of additional images are selected using the first classifier from a source of additional images accessible to the computing device. The first set of training images and the set of additional images are merged using the computing device to create a second set of training images. A second classifier is trained using a second process running on the computing device using the second set of training images. A set of unclassified images are classified using the second classifier thereby creating a set of classified images. The first classifier and the second classifier employ different classification methods.

System and method for improved classification

A search engine determines a set of other images that are similar to a user-selected image, and presents those other images to the user. In determining whether two images are sufficiently similar to each other to merit presentation of one, the search engine determines a Euclidean distance between separate feature vectors that are associated with each of the images. Each such vector indicates diverse types of information that is known about the associated image. The types of information included within such a vector may include attributes that reflect visual characteristics that are visible in an image, verbal tags that have been associated with the image users in a community of users, concepts derived from those tags, coordinates that reflect a geographic location at which a camera that produced the image was when the camera produced the image, and concepts related to groups with which the image is associated.

Image similarity from disparate sources

The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance metric and a distance-based function approximator estimating long-range expected value are then initialized, where the distance metric computes a distance between two (state, action) pairs, and the distance metric and function approximator are adjusted such that a Bellman error measure of the function approximator on the set of exemplars is minimized. A management policy is then derived based on the trained distance metric and function approximator.

Method and apparatus for improved reward-based learning using adaptive distance metrics

Methods and system for rendering context aware multimedia content include identifying a plurality of multimedia content that is uploaded for rendering. The uploaded multimedia content is examined to determine metadata associated with each of the plurality of multimedia contents. Contextual information associated with the metadata is identified and a grouping of the multimedia content into a plurality of groups is performed based on the contextual information. Each of the plurality of groups is then integrated into one or more photo stories. The photo stories are defined and rendered as content rich documents.

Context aware image representation

Embodiments are directed towards using a community of weighted results from local and global message classifiers to determine whether a message is spam. Each local classifier may receive a message that is to be evaluated to determine whether it is spam. A local classifier receives the message and performs a classification of the message. The local classifier may receive predictions of whether the message is spam from at least one global classifier. The local and global predictions are combined using, in one embodiment, a regression analysis to generate a single local message classification. Combining the local and global predictions is directed towards enabling a community of predictions to be used to classify messages. The user may then re-classify this output, which in turn is used as feedback to modify weights to the local and received global predictions for a next message.

Distributed spam filtering utilizing a plurality of global classifiers and a local classifier

The present invention is a method and an apparatus for reward-based learning of management policies. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance measure between pairs of exemplars is used to compute a Non-Linear Dimensionality Reduction (NLDR) mapping of (state, action) pairs into a lower-dimensional representation, thereby producing embedded exemplars, wherein one or more parameters of the NLDR are tuned to minimize a cross-validation Bellman error on a holdout set taken from the set of one or more exemplars. The mapping is then applied to the set of exemplars, and reward-based learning is applied to the embedded exemplars to obtain a learned management policy.

Method and apparatus for improved reward-based learning using nonlinear dimensionality reduction

The present invention is a method and an apparatus for improved regression modeling to address the curse of dimensionality, for example for use in data analysis tasks. In one embodiment, a method for analyzing data includes receiving a set of exemplars, where at least two of the exemplars include an input pattern (i.e., a point in an input space) and at least one of the exemplars includes a target value associated with the input pattern. A function approximator and a distance metric are then initialized, where the distance metric computes a distance between points in the input space, and the distance metric is adjusted such that an accuracy measure of the function approximator on the set of exemplars is improved.

Growing community of inventors

Mountain View, CA, United States of America

Kilian Quirin Weinberger