205 results about "Attribute weight" patented technology

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

A weighted combination of attributes including but not limited to color, shape, pattern, brand, style, size, and material may be used to rank items when searching for similar items of a given category on an ecommerce system website. The weights on each attribute may be optimized by using feedback from users, the feedback indicating the importance of each attribute to a purchaser when searching for similar items of various categories. Alternatively, click-through patterns from the ecommerce system website can be mined and used to optimize the importance of each attribute to users by item category. The optimum attribute weights for that items category are used to rank the results in a search for similar items in that category.

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations.

A method, system and computer program product is disclosed for intelligent data mining. The method comprises receiving an event from an application, assigning property weights to properties of the event, and building a query from these properties based on the property weights. The method further comprises assigning search engine weights to a group of search engines, selecting at least some of the search engines based on the search engine weights, and sending the built query to the selected search engines. Results from the selected search engines are stored in a knowledge repository and used to adjust the property weights and the search engine weights. The invention may be used to provide an analysis with information about a problem, and to manage a solutions database which can be used for problem determination. The invention provides a low cost solution for collecting relevant information from online sources.

Typical inventive practice provides for electronic communication of a computer with a display, an active radar device (for transmitting target-location data and environmental data), a light measurement device (for transmitting visual light data), and passive imaging devices covering bands in the visual, infrared (MWIR and / or LWIR), and millimeter wave regions of the electromagnetic spectrum. Inventive software in the computer's memory establishes “operational modes.” Each operational mode is defined at least by a predominant environmental (obscuration and lighting) character, ascribes “modal indices” to individual imaging devices, and carries its own multispectral image fusion algorithm (which, pursuant to the ascribed modal indices, attributes weights to the imaging data from the respective imaging devices). The inventive software aims the imaging devices toward the target, selects an operational mode (based on the environmental data), and executes the image fusion algorithm associated with the selected operational mode so that a fused multispectral image is displayed.

The invention provides a method, a device and a search engine for sequencing searching keywords, belonging to the field of data mining. The method comprises the following steps: collecting the searching log information and the attribute information of users, wherein the searching log information includes user marks and the searching keywords, and the attribute information includes the user marks and user attributes; establishing the congruent relationship between the user attributes and the searching keywords according to the searching log information and the attribute information; computing an attribute weighted value of each searching keyword by a predetermined searching keyword weighted formula according to the congruent relationship between the user attributes and the searching keywords; and sequencing the searching keywords according to the attribute weighted values of the searching keywords. The embodiment of the invention can accurately and fast mine data information which not only has higher searching heat but also represents searching habits of various user groups, thereby obtaining the information capable of really reflecting the searching habits of network users so as to provide data support for further knowing and meeting requirements of the users.

The present invention discloses a method for pedestrian weight recognition in a video surveillance scene. The method is implemented through the following steps: S1, pre-training an FT-FNN network; S2, fine-tuning the FT-FNN network; S3, extracting a depth feature and an attribute feature of a training image; S4, optimizing the attribute weight; S5, extracting the attribute feature of a to-be-recognized image; S6, extracting the attribute feature of a pedestrian bank; S7, generating a distance matrix; and S8, outputting matching images in sequence. The depth feature after fine-tuning has strong distinguishing ability for the pedestrian weight recognition; compared with the texture, color and other low-level visual features, the middle-level semantic attributes are stable and not easy to change greatly with the change of the light and the posture; and manual integration of low-level vision features can improve the distinguishing ability of the depth feature to a certain extent, and can improve accuracy of some middle-level attributes closely related to the color and the texture, so that by combining the three advantages, the method disclosed by the present invention achieves better accuracy in the field of pedestrian weight recognition.

The invention provides a power user grouping method based on text mining. The method is characterized by mainly through an automatic text classification model based on machine learning, establishing a label system database; according to a label and a label attribute weight, using a K-means cluster algorithm pair to carry out user portrait; and according to several label attributes under a user portrait, clustering users according to a certain rule, and using a Jaccard similarity calculating method to realize client group division based on the user portrait.

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

The invention discloses a program recommending method and a streaming media program system, wherein the method comprises the following steps that: the attribute weight of a program is generated according to the program playing behaviors of a user; and the program is recommended to the user according to the attribute weight of the program. The streaming media business system comprises a business management platform and a database, the business management platform comprises a user playing behavior analysis module, the user playing behavior analysis module is connected with the database, generates the attribute weight of the program according to the program playing behavior of the user, and recommends the program to the user according to the attribute weight of the program. Compared with the prior art, the invention calculates the preference of the user to programs by analyzing the program playing behaviors of the user according to the program attribute, recommends programs to the user and improves the recommendation precision.

The invention provides a TOPSIS-method multi-target threat ordering method based on information entropy. The method includes first building a threat assessment threat factor model, building a target attribute decision matrix according to the threat factor model, adopting an entropy weight method to calculate a target attribute weight vector according to the decision matrix, adopting a TOPSIS method to calculate relative close degree of target schemes according to the weight vector calculated through the entropy weight method and conducting threat assessment ordering on the schemes according to the close degree, namely the threat degree. By means of the method, shortcomings of the traditional ideal solution approaching ordering method in determination of weight coefficients are effectively overcome, and the target threat degree prediction accuracy is improved.

Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations.

The invention discloses a method and a device for selecting a network based on a converged heterogeneous radio access network. The network selection method comprises the following steps of: transforming the attribute evaluation value of each attribute of the spare network to respective intuition fuzzy number according to the network selection state information and the heterogeneous radio converged network information; obtaining a fuzzy multi-attribute decision matrix according to the respective intuition fuzzy number and further obtaining a normalized decision matrix; then, obtaining the optimal attribute weight by using the maximum total difference policy of all the attributes between the attribute evaluation values of all the spare network; and weighting the normalized decision matrix by using the optimal attribute weight vector, processing the optimal normalized weighting fuzzy policy matrix based on a possibility and sorting to obtain the optimal network. By the network selection method and device, the stability of the network selection mechanism is improved without influence on the network selection accuracy.

The present invention provides a group user profiling method. The method comprises: according to a label attribute weight library that comprises a label, an attribute under the label and a reference attribute weight, detecting a user behavior of each user among target group users, and applying a corresponding label; with reference to the label attribute weight library, determining attribute weights of all kinds of attributes under all kinds of labels of all users among the target group users, performing weighted averaging on the attribute weight according to a category of the attribute, and determining attribute weights of the target group users under all kinds of attributes; and according to the determined attribute weights of the target group users under all kinds of attributes, obtaining group user portraits of the target group users. The present invention further provides a group user profiling system. Precision of the group user portraits can be effectively improved.

A system that generates an item-to-item similarity for a category that includes a plurality of products receives attribute values for each product in the category and product-store-week sales units for each product in the category. The system estimates attribute weights. The system then determines the item-to-item similarity as a weighted attribute match score.

The invention discloses an intelligent mobile terminal data storage and backup method and system based on multi-cloud storage. The method comprises the steps that firstly, an intelligent mobile terminal selects a plurality of cloud disks to carry out the authorization authentication, and an authorized cloud disk set R is obtained; secondly, a multi-cloud storage scheme planner converts the attribute information of a file to be stored and backed up into a storage vector U, and a user requested cloud disk attribute weight vector W is calculated according to U; thirdly, the multi-cloud storage scheme planner searches the set R for a cloud disk subset R' with the residual space suitable for the file to be stored and backed up, for all cloud disks in R', a cloud disk weight vector Qw is generated according to the expectation value vector mu for attributes of the cloud disks of a user, the final weight of the cloud disks is calculated according to Qw and W, m cloud disks are selected from the set R' as a multi-cloud storage system, the reliability index P of the system is calculated, if P is larger than or equal to Fp, the file to be stored and backed up is stored into the candidate multi-cloud storage system, and Fp is the reliability index set by the user.

A method of utilizing and inventorying variable attributes for rooms associated with Hotels and other similar services. A database comprising multiple data sets is compiled, where the data sets available may comprise any of the following: room availability; room attributes; attribute weight; and Guest weight. The attribute weight reflects the perceived value of the attributes for a particular room. The Guest weight reflects the perceived value of the Guest as a continued business relationship. The data sets are combined, with the information comprising an inventory of data that is similar to available assets. When a room is requested with certain attributes, higher weighted Guests will be given rooms with a higher weight, and lower weighted guests are matched with rooms having a lower attribute weight. The system can continuously shuffle all rooms, prior to the Guest actually checking in, so as to maximize Guest satisfaction and revenue to the Hotel.

The invention discloses a node-similarity-based network community division method in a network. The method comprises the following steps: extracting information of network community division preferences of users and determining weights of attribute; acquiring datasets from user profiles in the network; taking each node as a network community, calculating the similarity between different network communities, and making network community pairs generate a max heap according to the similarity; selecting node pairs with the maximum similarity and combining the node pairs; calculating the similarity between a new network community and the other network communities, inserting the new network community in the max heap, and deleting related records of the combined network communities from the max heap; calculating the Q values of new network community divisions; repeating the steps til all the nodes are combined into the same network community, and finding out the extreme points of the Q values and corresponding divisions; modifying the weights of attribute, and repeating the arithmetic; and collecting the extremes of the Q values at the evaluation of the different weights and corresponding network divisions, and recommending the divisions with higher Q values to the users.

A method of optimizing parameter values in a process for producing a product, which process is essentially controlled by a set of parameters affecting a set of properties characterizing the product. The method uses an analytic hierarchy process (AHP) to associate a weight with each property according to its relative importance to obtain desired product characteristics. The method also uses parameter data and measured property data from a required number of experimental runs of the process, from which data property behavior relations between each property and the parameters are statistically established, which relations give estimated property values. Using the property weights, a process goal function is established, which is expressed in terms of weighted deviations between the estimated property values and the corresponding goal values for the properties. Finally, the process goal function is minimized in order to generate a set of optimal parameter values for the process.

The invention discloses a software credibility measurement and estimation method based on data mining, and the method comprises the following steps of: (1) on the basis of a concept hierarchy, defining a construction relation of each attribute weight and an adjacent layer attribute set in the same attribute set; (2) based on a software testing environment, providing a credibility computing base operator facing to an attribute feature set by combining with an artificial intelligence theory; (3) based on a software blackbox testing method, combining a data mining theory, obtaining a software credibility static state attribute feature set and a credibility computing base operator by using the blackbox testing, computing the initial credibility of the software layer by layer, obtaining a software dynamic attribute feature set by testing the software when running, and obtaining the credibility of the software in running by using a congruence measurement operator; (4) defining the lowest subjective expectation to the attribute feature set through a user, and computing the credibility of the software when in termination layer by layer; and (5) providing an estimation recommendation to the credibility of the to-be-estimated software.

The invention discloses a seam hole reservoir body detection method and detection device. The detection method includes the steps that multiple effective attributes of a target to be detected are selected; a mathematical expectation value of a known reservoir body is output, an overdetermined equation is established for all the effective attributes in combination with linear combination of attribute values of the effective attributes, and the least square solutions of the overdetermined equation are solved to serve as attribute weighing coefficients of the effective attributes; according to the attribute weighing coefficients, multiple preferred attributes are selected from the multiple effective attributes; according to the attribute weighing coefficients and the preferred attributes, fusion of the preferred attributes is achieved with a linear weighing method. The detection method and detection device solve the technical problem that when information parameters in a single seismic attribute are used for detecting complicated seam hole type reservoir stratums, the detection is not accurate, and the detection method and detection device have the advantage that when a complex reservoir body is detected through multi-attribute information, the reservoir body can be predicted more accurately.

The invention provides a Bayes classification-based method for fusing traditional meteorological data with perception data. On the basis of a Naive Bayes classifier, the invention discloses a One-Dependence Attribute Weighted Naive Bayes method, to improve a conventional Naive Bayes algorithm, appropriately release the limit that attributes need to be independent from each other, and find a compromising point between the efficiency and the classification efficiency, so as to accomplish the fusion of radar data with user perception data. The method comprises the following steps of: preprocessing the data; constructing the classifier according to training sample data; and classifying samples to be classified by using the constructed classifier.

The invention, which relates to the technical field of the electronic commerce, discloses a commodity displaying method and apparatus and an electronic commerce platform. The method comprises: commodity data are collected and are packaged as commodity display data that are then stored; a display instruction is received and a commodity display data set corresponding to a display dimensionality carried by the display instruction are extracted; commodity attribute weight information is obtained and a display priority coefficient of the commodity display data in the commodity display data set is calculated based on the commodity attribute weight information; and according to the display priority coefficient, the commodity display data in the commodity display data set are displayed. According to the invention, the commodity data are obtained and are packaged as the commodity display data; the display priority coefficient is calculated based on the commodity attribute weight information; the data are displayed based on the display priority coefficient; and thus an optimal commodity can be calculated and intelligent displaying can be carried out. When a user is buying a commodity, comparison and selection reference information can be provided, so that the user experience is improved.

The invention relates to a subjective-and-objective mixed evaluation method for constructing a power system cloud platform. The method is characterized by comprising the following steps of1, analyzinginfluence factors for constructing the power system cloud platform, and constructing a power system cloud platform construction and evaluation index system; 2, solving an index weight value by meansof the subjective-and-objective mixed weight determining method in which a hierarchy analysis method and an entropy weight method are combined based on the power system cloud platform construction andevaluation index system which is established in the step 1, and establishing a subjective-and-objective mixed evaluation model for constructing the power system cloud platform; and 3, performing comprehensive evaluation on construction of the power system cloud platform, and obtaining a power system cloud platform construction evaluation valve F. According to the subjective-and-objective mixed evaluation method, the entropy weight method and the hierarchy analysis method are combined; the subjective-and-objective mixed weight determining method in which the hierarchy analysis method and an entropy weight method are combined is selected for solving the weight determining value; and defects of overhigh subjectivity of an expert option in a subjective weight determining method and overhigh randomness of measured data in a subjective weight determining method.