91 results about "Model interpretation" patented technology

A model-driven approach to business performance management (BPM) uses a hybrid compilation-interpretation approach to map an observation model to a runtime executable. The data aspect of the observation model is first extracted and refactored to facilitate runtime access. Next, the operational aspect of the model, such as logic for metric computation and situation detection, is compiled into code. Finally, a runtime engine interprets the refactored model and dynamically loads the generated code, according to the meta-model.

A computer program based method is described for creating models using gestures. On an input device, such as an electronic whiteboard, a user draws a gesture which is recognised by a computer program and interpreted relative to a predetermined meta-model. Based on the interpretation, an algorithm is assigned to the gesture drawn by the user. The executed algorithm may, for example, consist in creating a new model element, modifying an existing model element, or deleting an existing model element.

A computing system, comprising: an I/O platform for interfacing with a user; and a processing entity configured to implement a dialog with the user via the I/O platform. The processing entity is further configured for: identifying a grammar template and an instantiation context associated with a current point in the dialog; causing creation of an instantiated grammar model from the grammar template and the instantiation context; storing the instantiated grammar model in a memory; and interpreting user input received via the I/O platform in accordance with the instantiated grammar model. Also, a grammar authoring environment supporting a variety of grammar development tools is disclosed.

The invention discloses a self-paced-cooperative training learning method. The method comprises the following steps of: obtaining data, from two visual fields, of a target problem, and initializing a model, wherein the data comprises a small amount of labeled data and a large amount of unlabeled data; respectively determining corresponding optimization targets on the two visual fields; embedding a self-paced regular term in a loss function of each visual field so as to realize steady learning under the visual field; associating the two visual fields through a regular term; obtaining a multi-visual field semi-supervised self-paced-cooperative training model which is embedded into a steady learning mechanism and has model interpretation; and obtaining high-quality labeling of the unlabeled data by applying the small amount of labeled data and large amount of unlabeled data in a target field and a semi-supervised multi-visual point learning model, and obtaining reliable learning devices under the two visual fields at the same time. The invention aims at providing a steady learning model with a replacement mode for the traditional cooperative training algorithms to ensure that data lack of labeling in the target field can obtain more correct and high-quality labeling.

The invention provides a machine learning model interpretation method and device, and the method comprises the steps: determining the model classification of a machine learning model; Explaining features in the machine learning model by adopting a model explanation method matched with the model classification; And comparing the explanation information of the feature obtained by explanation with reference explanation information to obtain an explanation effect of the feature. According to the machine learning model interpretation method provided by the invention, by understanding the decision basis of the machine learning model, the characteristic of high value of the machine learning model is screened out, and the characteristic screening quality of the machine learning model is improved.

The embodiment of the invention provides a method and device for evaluating a model interpretation tool, and the method comprises the steps: training a first model by adopting a plurality of first training samples so as to obtain the first model with a first parameter group; acquiring the first performance value of the first model with the first parameter group based on a plurality of test samples; based on the plurality of first training samples and the first parameter group, obtaining importance sequences of a plurality of features through a model interpretation tool; replacing the feature values of the features except the first n features of the importance sequence in each first training sample with the same predetermined value to obtain a plurality of second training samples; trainingthe first model by using the plurality of second training samples to obtain a first model with a second parameter group; acquiring the second performance value of the first model with the second parameter group based on the plurality of test samples; and calculating a difference value between the first performance value and the second performance value for evaluating the model interpretation tool.

The invention provides an interpretation scheme for lift tree model transfer learning, which can interpret the transfer learning based on the lift tree model and can support local interpretation and global interpretation of the transfer model.

The invention discloses a model interpretation method and equipment and a readable storage medium. The model interpretation method comprises the following steps of obtaining each model input feature corresponding to a preset black box model, inputting the prediction data set corresponding to each model input feature into a first hash coding model optimized based on each preset sample category; carrying out hash coding on the prediction data set; obtaining a first hash coding result, inputting the prediction data set into a second hash coding model optimized based on each output sample categoryoutput by the preset black box model; and performing hash encoding on the prediction data set to obtain a second hash encoding result, and further determining feature confidence corresponding to eachmodel input feature based on each bit difference degree between the first hash encoding result and the second hash encoding result. The technical problem that the model interpretation effect is pooris solved.

The invention discloses a DOA estimation method based on a machine learning algorithm XGBoost. The DOA estimation method comprises the following steps: acquiring a noisy array signal to obtain a covariance matrix; obtaining a data set according to the covariance matrix; constructing a training set and a test set according to the data set; training the training set by adopting an XGBoost algorithmmodel, and calculating optimal parameters of the model; and predicting the test set according to the optimal parameters of the XGBoost algorithm model. According to the DOA estimation method based onthe machine learning algorithm XGBoost provided by the invention, the prediction speed and precision are improved; meanwhile, the method has the advantages that the method is not easily influenced byabnormal values, a large amount of training data is not needed and the model interpretability is good.

The invention discloses an object-oriented multi-level interpretation method and system for a live-action three-dimensional model. A to-be-interpreted live-action three-dimensional model is divided into a terrain surface part and a three-dimensional ground object part; the terrain surface part is classified by adopting an object-oriented image classification method based on the orthoimage of the terrain surface part; independent ground object monomerization processing is carried out on the three-dimensional ground object part, and based on the characteristics of independent ground object monomers, a machine learning algorithm is adopted to classify the three-dimensional ground object part; and classification results of the terrain surface part and the three-dimensional ground object part are integrated to obtain an interpretation result of the whole live-action three-dimensional model. According to the method, the live-action three-dimensional model is divided into two layers to be classified respectively, so that the model interpretation difficulty is reduced; an object-oriented model interpretation method is adopted, so that other salt and pepper noise phenomena generated in a classification process based on a patch algorithm can be improved; based on a multi-dimensional feature fusion strategy of geometry, texture and spectrum, the accuracy and robustness of a classificationresult are improved.

The invention discloses a hybrid indexing method based on big-data model metadata. The method comprises the following steps: S1, extracting hybrid index metadata, wherein the metadata of a hybrid index is extracted according to the big-data model metadata and model interpretation and operation characteristics, and a value of 1 or 0 is assigned to the metadata according to an attribute value of the metadata; S2, constructing or updating the hybrid index, wherein a global hash function is utilized to construct or update the hybrid index; S3, storing the hybrid index, wherein all parts of the hybrid index are stored into a memory, a cache and a disk according to hybrid index features, and index contents are sequentially and concurrently retrieved according to a query request; and S4, retrieving the hybrid index, wherein a retrieval algorithm is constructed according to big-data model features and hybrid index characteristics, and the different parts of the hybrid index are retrieved at the same time. According to the method, the big-data model metadata and the model characteristics are tightly integrated, a high-efficient and accurate model indexing technology is provided, the retrieval speed is increased, and the convenience of big-data model using is improved.

The invention discloses a health state detection method and device based on model interpretation, and a readable storage medium. The health state detection method based on model interpretation comprises the following steps: acquiring to-be-detected user data corresponding to a target user, inputting the to-be-detected user data into a preset health state prediction model, and detecting the healthstate of the target user to acquire an initial detection result of a health state; and performing model prediction interpretation on the initial detection result of the health state to obtain a healthstate influence factor corresponding to the initial detection result of the health state, and generating a target health state detection result corresponding to the target user based on the health state influence factor and the initial detection result of the health state. According to the invention, the technical problem of low confidence degree of health state detection is solved.

The invention provides a stand growth and yield model dynamic state management method based on an encryption technique, and relates to the technical field of forestry resource management and forestry informatization. The method comprises the following steps: S1, a stand growth and yield model table is built; S2, the symmetric encryption technique DES (data encryption standard) is used for performing encryption to a model stored in the stand growth and yield model table, and a cipher text generated through encryption is written in the stand growth and yield model table; S3, the symmetric encryption technique DES is used for performing decryption to the cipher text read in the stand growth and yield model table, and the plaintext generated through decryption is displayed to a user; S4, the structured query language (SQL) is used for performing analysis to a model in the plaintext. The method builds a stand growth and yield model table, closely related to the origin, of the same area, uses the encryption technique to perform management to the access of data in the model table, provides a fast and simple model interpretation method, and finally realizes flexile and safe dynamic management of the stand growth and yield model.

The invention discloses a model interpretation method and device and a readable storage medium. The method comprises the steps: obtaining to-be-interpreted sample data, and distributing the sample data to all execution units in a cluster; broadcasting a to-be-interpreted model to each execution unit, so that each execution unit calculates the contribution degree of the target sample data to the prediction result of the to-be-interpreted model based on a preset model interpretation algorithm after receiving the to-be-interpreted target sample data. According to the method, the model interpretation efficiency is improved, and the method can be used for processing model interpretation tasks of more complex service scenes.