Abstract: A spam detection system is disclosed. The system includes a classifier training component that receives a first set of training pages labeled as normal pages and a second set of training pages labeled as spam pages. The training component trains a web page classifier based on both the first set of training pages and the second set of training pages. A spam detector then receives unlabeled web pages uses the web page classifier to classify the unlabeled web pages as spam pages or normal pages.

Abstract: The concept of variability pertains to whether users exhibit consistent search interaction patterns, for example, in terms of interaction flow or information targeted. Methods are provided for analyzing variability, and then adapting search-related functionality (e.g., processes and/or interfaces) to account for variability characteristics, for example, to account for predictable search interaction behavior.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: The subject disclosure pertains to systems and methods for facilitating training of machine learning systems utilizing pairwise training. The number of computations required during pairwise training is reduced by grouping the computations. First, a score is generated for each retrieved data item. During processing of the data item pairs, the scores of the data items in the pair are retrieved and used to generate a gradient for each data item. Once all of the pairs have been processed, the gradients for each data item are aggregated and the aggregated gradients are used to update the machine learning system.

Abstract: The subject disclosure pertains to Internet or web search engines and more particularly toward a media service integrated or associated therewith. The media service can identity particular media queries as well as provide and present useful information in an easily accessible manner. The search engine can therefore provide a common ground for people to locate things they perceive with respect to media (e.g., advertisements, music, video . . . ) and further facilitate purchase of media items and/or related content or items.

Abstract: Structured content and associated metadata from the Web are leveraged to provide specific answer string responses to user questions. The structured content can also be indexed at crawl-time to facilitate searching of the content at search-time. Ranking techniques can also be employed to facilitate in providing an optimum answer string and/or a top K list of answer strings for a query. Ranking can be based on trainable algorithms that utilize feature vectors for candidate answer strings. In one instance, at crawl-time, structured content is indexed and automatically associated with metadata relating to the structured content and the source web page. At search-time, candidate indexed structured content is then utilized to extract an appropriate answer string in response to a user query.

Abstract: The subject disclosure pertains to systems and methods for training machine learning systems. Many cost functions are not smooth or differentiable and cannot easily be used during training of a machine learning system. The machine learning system can include a set of estimated gradients based at least in part upon the ranked or sorted results generated by the learning system. The estimated gradients can be selected to reflect the requirements of a cost function and utilized instead of the cost function to determine or modify the parameters of the learning system during training of the learning system.

Abstract: A general probabilistic formulation referred to as ‘Conditional Harmonic Mixing’ is provided, in which links between classification nodes are directed, a conditional probability matrix is associated with each link, and where the numbers of classes can vary from node to node. A posterior class probability at each node is updated by minimizing a divergence between its distribution and that predicted by its neighbors. For arbitrary graphs, as long as each unlabeled point is reachable from at least one training point, a solution generally always exists, is unique, and can be found by solving a sparse linear system iteratively. In one aspect, an automated data classification system is provided. The system includes a data set having at least one labeled category node in the data set. A semi-supervised learning component employs directed arcs to determine the label of at least one other unlabeled category node in the data set.

Abstract: The present invention relates to a system and methodology to facilitate database processing in accordance with a plurality of various applications. In one aspect, a large database of objects is processed, wherein the objects can be represented as points in a vector space, and two or more objects are deemed ‘close’ if a Euclidean distance between the points is small. This can apply for substantially any type of object, provided a suitable distance measure can be defined. In another aspect, a ‘test’ object having a vector x, is processed to determine if there exists an object y in the database such that the distance between x and y falls below a threshold t. If several objects in the database satisfy this criteria, a list of objects can be returned, together with their corresponding distances. If no objects were to satisfy the criterion, an indication of this condition can also be provided, but in addition, the condition or information relating to the condition can be provided.

Abstract: A machine learning system to rank data within sets is disclosed. The system comprises a ranking module that has differentiable parameters. The system further comprises a cost calculation module that uses a cost function that depends on pairs of examples and which describes an output of the ranking module. Methods of using the disclosed system are also provided.

Abstract: A unique system and method that facilitates improving the ranking of items is provided. The system and method involve re-ranking decreasing subsets of high ranked items in separate stages. In particular, a basic ranking component can rank a set of items. A subset of the top or high ranking items can be taken and used as a new training set to train a component for improving the ranking among these high ranked documents. This process can be repeated on an arbitrary number of successive high ranked subsets. Thus, high ranked items can be reordered in separate stages by focusing on the higher ranked items to facilitate placing the most relevant items at the top of a search results list.

Abstract: The present invention generally relates to microRNAs such as vertebrate microRNA (miRNA), for example, mammalian miRNA. Various aspects of the invention are directed to the detection, production, or expression of miRNA. In one aspect, the invention provides systems and methods for identifying targets of miRNA sequences. For instance, in one embodiment, gene sequences comprising UTRs are compared with miRNA sequences to determine the degree of interaction, for example, by determining a free energy measurement between the miRNA sequence and the UTR, and/or by determining complementarity between at least a portion of the miRNA sequence and the UTR. In another aspect, the invention is directed to the regulation of gene expression using miRNA. For example, gene expression within a cell may be altered by exposing the cell to an oligonucleotide comprising a sequence that is substantially antisense to at least a portion of an miRNA region of the gene, for example, antisense to a 6-mer or 7-mer portion of the miRNA.

Abstract: Systems and methods are disclosed that facilitate producing probabilistic outputs also referred to as posterior probabilities. The probabilistic outputs include an estimate of classification strength. The present invention intercepts non-probabilistic classifier output and applies a set of kernel models based on a softmax function to derive the desired probabilistic outputs. Such probabilistic outputs can be employed with handwriting recognition where the probability of a handwriting sample classification is combined with language models to make better classification decisions.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: Prior to searching a multidimensional feature space populated with data objects, each dimension in the feature space is divided into a number of intervals. When a query is received, a single interval that is overlapped by the query is selected from each dimension. A reduced set of data objects is then selected that includes only those data objects that overlap the selected intervals. This reduced set of data objects, rather than the entire set of data objects in the feature space, is then used to determine matches for the query.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: Prior to searching a multidimensional feature space populated with data objects, each dimension in the feature space is divided into a number of intervals. When a query is received, a single interval that is overlapped by the query is selected from each dimension. A reduced set of data objects is then selected that includes only those data objects that overlap the selected intervals. This reduced set of data objects, rather than the entire set of data objects in the feature space, is then used to determine matches for the query.

Abstract: The present invention relates to a system and methodology to facilitate database processing in accordance with a plurality of various applications. In one aspect, a large database of objects is processed, wherein the objects can be represented as points in a vector space, and two or more objects are deemed ‘close’ if a Euclidean distance between the points is small. This can apply for substantially any type of object, provided a suitable distance measure can be defined. In another aspect, a ‘test’ object having a vector x, is processed to determine if there exists an object y in the database such that the distance between x and y falls below a threshold t. If several objects in the database satisfy this criteria, a list of objects can be returned, together with their corresponding distances. If no objects were to satisfy the criterion, an indication of this condition can also be provided, but in addition, the condition or information relating to the condition can be provided.