Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

Abstract: The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization through the use of multiple processors may improve learning speed. The disclosure describes effective systems for distributed multiclass classification learning on several processors. These systems are applicable to multiclass models where the training process may be split into training of independent binary classifiers.

Abstract: A method and apparatus based on transposition to speed up learning computations on sparse data are disclosed. For example, the method receives an support vector comprising at least one feature represented by one non-zero entry. The method then identifies at least one column within a matrix with non-zero entries, wherein the at least one column is identified in accordance with the at least one feature of the support vector. The method then performs kernel computations using successive list merging on the at least one identified column of the matrix and the support vector to derive a result vector, wherein the result vector is used in a data learning function.

Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

Abstract: A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.

Abstract: Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word.

Abstract: A method and apparatus for using a classifier for processing a query are disclosed. For example, the method receives a query from a user, and processes the query to locate one or more documents in accordance with a search engine having a discriminative classifier, wherein the discriminative classifier is trained with a plurality of artificial query examples. The method then presents a result of the processing to the user.

Abstract: A system to detect anomalies in internet protocol (IP) flows uses a set of machine-learning (ML) rules that can be applied in real time at the IP flow level. A communication network has a large number of routers that can be equipped with flow monitoring capability. A flow collector collects flow data from the routers throughout the communication network and provides them to a flow classifier. At the same time, a limited number of locations in the network monitor data packets and generate alerts based on packet data properties. The packet alerts and the flow data are provided to a machine learning system that detects correlations between the packet-based alerts and the flow data to thereby generate a series of flow-level alerts. These rules are provided to the flow time classifier. Over time, the new packet alerts and flow data are used to provide updated rules generated by the machine learning system.

Abstract: A method and apparatus based on transposition to speed up learning computations on sparse data are disclosed. For example, the method receives an support vector comprising at least one feature represented by one non-zero entry. The method then identifies at least one column within a matrix with non-zero entries, wherein the at least one column is identified in accordance with the at least one feature of the support vector. The method then performs kernel computations using successive list merging on the at least one identified column of the matrix and the support vector to derive a result vector, wherein the result vector is used in a data learning function.

Abstract: A method of creating an animated entity for delivering a multi-media message from a sender to a recipient comprises receiving from the sender an image file to a server, the image file having associated sender-assigned name, gender, category and indexing information. The server presents to the sender the image file and a group of generic face model templates. After the sender selects one of the generic face model templates, the server presents the image file and the selected model template to the sender and requests the sender to mark features on the image file. After the sender marks the image file, the server presents to the sender a preview of at least one expression associated with the marked image file. If the user does not accept the image file after the preview, the server presents again the image file and selected model template for the sender to redo or add marked features on the image file.

Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

Abstract: A method and apparatus based on transposition to speed up learning computations on sparse data are disclosed. For example, the method receives an support vector comprising at least one feature represented by one non-zero entry. The method then identifies at least one column within a matrix with non-zero entries, wherein the at least one column is identified in accordance with the at least one feature of the support vector. The method then performs kernel computations using successive list merging on the at least one identified column of the matrix and the support vector to derive a result vector, wherein the result vector is used in a data learning function.

Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

Abstract: The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization thought the use of multiple processors may improve learning speed. The invention describes effective methods to distributed multiclass classification learning on several processors. These methods are applicable to multiclass models where the training process may be split into training of independent binary classifiers.

Abstract: Disclosed are email server management methods and systems that protect the ability of the infrastructure of the email server to process legitimate emails in the presence of large spam volumes. During a period of server overload, priority classes of emails are identified, and emails are processed according to priority. In a typical embodiment, the server sends emails sequentially in a queue, and the queue has a limited capacity. When the server nears or reaches that capacity, the emails in the queue are analyzed to identify priority emails, and the priority emails are moved to the head of the queue.

Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.

Abstract: Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word.

Abstract: Classification of sequences, such as the translation of natural language sentences, is carried out using an independence assumption. The independence assumption is an assumption that the probability of a correct translation of a source sentence word into a particular target sentence word is independent of the translation of other words in the sentence. Although this assumption is not a correct one, a high level of word translation accuracy is nonetheless achieved. In particular, discriminative training is used to develop models for each target vocabulary word based on a set of features of the corresponding source word in training sentences, with at least one of those features relating to the context of the source word. Each model comprises a weight vector for the corresponding target vocabulary word.

Abstract: In an embodiment, a method is provided for creating a personal animated entity for delivering a multi-media message from a sender to a recipient. An image file from the sender may be received by a server. The image file may include an image of an entity. The sender may be requested to provide input with respect to facial features of the image of the entity in preparation for animating the image of the entity. After the sender provides the input with respect to the facial features of the image of the entity, the image of the entity may be presented as a personal animated entity to the sender to preview. Upon approval of the preview from the sender, the image of the entity may be presented as a sender-selectable personal animated entity for delivering the multi-media message to the recipient.