176 results about "Temporal complexity" patented technology

The invention discloses a distributed knowledge data mining device and method used for a complex network. The distributed knowledge data mining device adopts a distributed computing platform which is composed of a control unit, a computing unit and a man-machine interaction unit, wherein the innovation key is to finish the calculated amount needed by a multifarious clustering algorithm in the data mining by different servers so as to improve the efficiency of the data mining. Aiming at different knowledge data, the degrees of relation and the weights of knowledge data also can be computed by applying different standards, so that a more credible result is obtained. A second-level clustering mode is adopted in the knowledge data mining process; the result of the first-level clustering is relatively rough, but the computing complexity is very low; and the computing complexity of the second-level clustering is relatively high, but the result is more precise. By combining the first-level clustering with the second-level clustering efficiently, the distributed knowledge data mining device improves the time complexity and clustering precision greatly in comparison with the traditional first-level clustering mode. According to the invention, as a visual and direct exhibition network structure and a dynamic evolutionary process are adopted, references are provided for the prediction in the fields of disciplinary development and hotspot research.

The invention discloses a method for identifying a named entity based on a policy value network, and belongs to the field of information processing. The method comprises the following steps of firstly, modeling a labeling process of named entity recognition into a Markov decision process (MDP) so that a model for identifying the named entity based on enhanced learning is provided, namely MM-NER. MM-NER is the first to apply the Monte Carlo Tree Search (MCTS) enhanced MDP model to named entity recognition (sequence tag task).A strategy value network is designed on the basis of an MDP state definition to obtain label probability and label sequence accuracy evaluation, and an MCTS is used for simulation, so that a label sequence with more global consciousness is searched out. In the inferenceprocess, the strategy value network is directly used to ensure that the identification effect is basically consistent with the tree search strategy, and the time complexity is greatly reduced. An experimental result of the present invention on the CoNLL2003 named entity identification data set demonstrates the effectiveness of the MM-NER with the K-step exploration decision mechanism.

The invention provides an attribute weighting method based on a decision tree. The attribute weighting method comprises the following steps: firstly, constructing the decision tree by an information gain rate standard, and then, calculating a weight according to the minimum depth of each attribute in a test in the decision tree. The invention simultaneously provides a polynomial nave Bayesian text classification method of the attribute weighting method based on the decision tree, a complementary set nave Bayesian text classification method of the attribute weighting method based on the decision tree and a polynomial and complementary set combination nave Bayesian text classification method of the attribute weighting method based on the decision tree. The attribute weighting method improves the classification precision of the original nave Bayesian text classifier and simultaneously maintains the conciseness and the time complexity of the original nave Bayesian algorithm.

The invention discloses a planning method and system for a shortest path. The method comprises the following steps of establishing an initial grid map according to the environmental information, and respectively marking the grid points of the grid map as a starting point, candidate points and obstacle points; traversing the candidate points to calculate the minimum step length from the candidate points to the starting point so as to form a minimum step length matrix graph; and selecting one of the candidate points as an ending point, and performing the recursive traversal through a reverse depth-first algorithm to obtain a shortest path plan from the ending point to the starting point. The method and the system of the scheme are based on a grid map global expansion algorithm, not only solve the problem that the final path is the shortest path, but also greatly reduce the time complexity and the space complexity of the operation.

The invention relates to a track similarity calculation method based on sorting learning. The method comprises the following steps: 1) expanding an Euclidean space; 2) calculating similar attributes of the track sequence; 3) normalizing the track sequence; 4) constructing a label of an experiment track by adopting a self-similarity method; 5) constructing a training set, and training the SVM learner; and 6) dividing the test set into an input document set and a label document set, and outputting the rank of the real label of each input document by applying the trained SVM sorting model. The beneficial effects of the invention are that the method irons out the limitations of a conventional track similarity calculation method based on a point matching algorithm, the low accuracy of track matching, the weak resistance to a noise non-uniform sampling rate, and the efficiency problem of a track matching algorithm based on deep learning; according to the method, the time complexity of similarity calculation is greatly reduced, and compared with the prior art, the accuracy of trajectory similarity calculation is improved.

The invention discloses a handwritten numeral recognition method based on point density weighting online FCM clustering. The method is used for processing the large-scale offline handwritten numeral recognition problem. The method includes the steps that (1), all handwritten numeral image sets are preprocessed; (2), clustering centers are initialized, and data points are made to sequentially enter processing procedures; (3), the membership degree of the current data point and all the clustering centers is calculated; (4), if the membership degree reaches a threshold value, the position of the nearest clustering center is updated; (5), if the membership degree does not reach the threshold value, the current data point is not processed and is temporarily placed in a to-be-processed region; (6), when the to-be-processed region reaches certain standards, data in the to-be-processed region are clustered through a point density weighting FCM algorithm, and the clustering centers are updated; (7), circulation continues until all the data points are processed; (8), the membership degrees of all the data points are calculated through acquired clustering center blocks, the data points are divided into different classes, and data classification is finished through scanning at a time. According to the method, the space complexity and the time complexity can be lowered from the aspect of processing the large-scale handwritten numeral recognition problem.

The invention provides a method for rapidly judging the relation between points and polygons. A traditional ray method is greatly improved. A differential idea is utilized to convert a graph into a polygon formed by point connection, a data structure capable of being quickly retrieved is newly built to store information of the points, and rays of a traditional ray method are converted into rays ofa traditional ray method. Line segment relation judgment and conversion into to-be-checked point The values between the data structures are searched and compared to obtain the intersection times of the rays and the graphs. according to the parity of the value, obtaining the position relation between the point to be checked and the polygon through a general rule of a ray method. The time complexity of the method is O (1), the method has no correlation with the number N of edges, and the performance of the method is greatly improved compared with that of a traditional method in calculation occasions with complex graphs and high precision requirements.

The invention relates to an efficient intelligent customer service method for a large corpus. According to the method, the recursion clustering algorithm based on keyword weighting is introduced to pre-classify the corpus, the advantage of lexicon matching is reserved based on keyword weighting, and a similarity calculation mode is adopted, so that the time complexity of similarity calculation isreduced. And meanwhile, the cost expenditure of manual marking and word library construction is avoided, and the complexity and the unsuitability of transfer learning do not exist. The system has goodperformance in the man-machine interaction fields of store automatic after-sale, citizen automatic inquiry, official account automatic reply automatic after-sale questions and answers, citizen cloudautomatic inquiry, social APP automatic chatting and the like.

The invention discloses an entity alignment method based on a heterogeneous graph attention network, and the method comprises the steps: dividing entities into different categories based on clustering; secondly, learning an embedded vector of an entity based on the heterogeneous graph attention network; calculating the similarity between different entity categories based on entity embedded vectors, and obtaining the similarity of any entity pair by combining the vector similarity; and finally, modeling an entity alignment problem into an integer programming problem, and solving and obtaining an alignment result meeting a one-to-one alignment constraint. The method is not only low in time complexity, but also high in entity alignment precision.

The invention discloses a multi-appearance model fusion target tracking method and device based on sparse representation. The method comprises the steps that a particle filtering framework is built; firstly, a target template set and a candidate target set of a tracking image are determined, and then the target template set and a trivial template are subjected to linear programming to generate a sparse coefficient solution; establishing a local sparse appearance model; fusing the global sparse appearance model and the local sparse appearance model: firstly calculating two similarity elements,then calculating a weighted sum of the two elements as fusion similarity, calculating a reconstruction error, and taking the weighted sum of the fusion similarity and the reconstruction error as a discrimination function; and endowing the target template of the target template set with a weight in direct proportion to the importance of the target template, and updating the target template of the target template set. According to the method, the time complexity is reduced, high adaptability is achieved under the conditions that the appearance of the target is greatly changed and the shielding area is large, the target is accurately and robustly tracked, the shielding processing capacity is achieved, and the appearance model updating efficiency is high.

The invention discloses a target detection method and device. The method comprises the steps: obtaining a plurality of bounding boxes on a target image and the scores of the bounding boxes, and enabling the scores to be used for representing the confidence of a target object contained in the bounding boxes; dividing the target image to obtain a plurality of grids, and determining the grids to which the plurality of bounding boxes belong; traversing the plurality of bounding boxes, calculating the overlapping degree of a reference bounding box and an adjacent bounding box, and obtaining a target bounding box according to the overlapping degree; and determining a target detection result according to the score of the target bounding box. According to the embodiment of the invention, grid division is carried out on the target image, the grid to which the bounding box belongs is determined, and then the adjacent relation of the bounding box is determined according to the adjacent relation between the grids, so that only the overlapping degree of the reference bounding box and the adjacent bounding box needs to be calculated during NMS algorithm operation, the time complexity of NMS is effectively reduced, and the calculation efficiency is improved. Target detection efficiency is improved.

The invention relates to a hierarchical interest matching method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining composition dimensions of a routing space where the ordering area and the updating area to be matched are located, carrying out mapping processing on the ordering area to be matched according to the composition dimension; obtaining a mapping ordering area carrying an ordering area identifier in each dimension; matching the updating area with the mapping ordering area in each dimension; and determining an ordering area identifier matched with the updating area in each dimension, determining a target ordering area identifier existing in each dimension according to the ordering area identifier matched with the updating area in each dimension, and determining the ordering area matched with the updating area according to the target ordering area identifier. Through dimension division and data mapping, the time complexity isreduced, only data reading of a mapping ordering area is involved in the matching process, locking is not needed, parallel matching processing is achieved, and the processing efficiency of the interest matching process is improved.

The invention discloses a firewall strategy search method based on spatial sorting, and compared with the prior art, the algorithm time complexity of the search method is only O(N/2+log<2>N), so thatthe algorithm time complexity O(N) of the existing search method is greatly reduced, and the calculation performance is remarkably improved. The invention also discloses a search system for realizingthe search method.