Abstract: After initial clusters having only one component are formed, a conditional probability P(Ci|C?k) is determined for the cluster Ci being included in an order on condition that cluster C?k is included in the order. If P(Ci|C?k) is greater than a first threshold value S1, a new cluster Cn having all the components of clusters Ci, C?k is formed and the operations are repeated until no new clusters are formed.

Abstract: The invention relates to a method for the computer-assisted identification of a class of VoIP calls of a first type (spam) in a communication network (internet). Said communication network has a plurality (N) of first subscribers (Tn1-1, . . . , Tn1-5) and a plurality (M) of second subscribers (Tn2-1, . . . , Tn2-7), the first and the second subscribers being allocated a definite characteristic (IP address, telephone number, e-mail address) wherein, at least some of the first subscribers (Tn1-1, . . . , Tn1-5) are allocated, respectively, with at least one list (white list, black list) which contains at least one definite characteristic of the second subscriber.

Abstract: Interacting entities are classified into cluster classes, where an interaction is a relation between two entities based on a promised outcome by each entity and an effective outcome of the interaction. A model for infinite relational trust is used which has hidden variables associated with entity classes corresponding to the entities. A conditional probability distribution of the hidden variables is calculated depending on observable attributes assigned to the entities and the relations.

Abstract: The invention provides a method for semantic relation extraction, wherein on the basis of an annotated training corpus having tokens with associated relational labels each indicating a relation between the respective token and a selectable key entity semantic relation between said key entity and other entities are directly extracted from unstructured text using a probabilistic extraction model.

Abstract: A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

Abstract: A method and a suitable x-ray device (1) for carrying out the method are specified for effective and operationally simplified user-specific optimization of a parameter configuration (K) of an x-ray device (1), said configuration comprising at least one recording parameter (U,I,t,F). It is accordingly provided that a user (20) is shown a plurality of reference images (V,V1) for different reference parameter sets (PV) from a reference memory (21) in which are stored a large number of reference images (V,V0) each with an associated reference parameter set (PV), that for each reference image (V,V1) shown the user (20) submits an assessment (BM) of the image quality (V,V1), and that on the basis of the submitted assessments (BM) an optimized parameter configuration (K) is created from the reference parameter sets (PV) of the reference images (V,V1) shown.

Abstract: After initial clusters having only one component are formed, a conditional probability P(Ci|C?k) is determined for the cluster Ci being included in an order on condition that cluster C?k is included in the order. If P(Ci|C?k) is greater than a first threshold value S1, a new cluster Cn having all the components of clusters Ci, C?k is formed and the operations are repeated until no new clusters are formed.

Abstract: The invention relates to a method and a system for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least on e of the state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by using the sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Abstract: A method and a suitable x-ray device (1) for carrying out the method are specified for effective and operationally simplified user-specific optimization of a parameter configuration (K) of an x-ray device (1), said configuration comprising at least one recording parameter (U,I,t,F). It is accordingly provided that a user (20) is shown a plurality of reference images (V, V1) for different reference parameter sets (PV) from a reference memory (21) in which are stored a large number of reference images (V, V0) each with an associated reference parameter set (PV), that for each reference image (V, V1) shown the user (20) submits an assessment (BM) of the image quality (V, V1), and that on the basis of the submitted assessments (BM) an optimized parameter configuration (K) is created from the reference parameter sets (PV) of the reference images (V, V1) shown.

Abstract: The invention relates to a method and an arrangement for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least one of said state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by means of said sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Abstract: In a method and apparatus for determining the circumference of a finger or toe joint, in particular a proximal interphalangeal joint, to be evaluated in the context of an arthritis examination of an examination subject, the joint is irradiated using a light source and at least one two-dimensional projection image is recorded using a camera apparatus. From the projection image, the diameter of the joint is determined by means of an automatic edge detection method, and the circumference is calculated on the basis of the diameter. Additionally, a diaphanoscopic examination of the joint can be made with the same apparatus, and the results combined with the diameter information to identify or monitor a degree or progress of the inflamation.

Abstract: The training phase of a neural network NN is stopped before an error function, which is to be minimized in the training phase, reaches a minimum (301). A first variable (EG) is defined using, for example, the optimal brain damage method or the optimal brain surgeon method, on the assumption that the error function is at the minimum. Furthermore, a second variable (ZG) is determined which provides an indication of the manner in which the value of the error function varies when a weight (wi) is removed from the neural network (NN). The first variable (EG) and the second variable (ZG) are used to classify the weight (wi) as being suitable or unsuitable for removal from the neural network (NN).

Abstract: In a method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior wherein only a few measured values of the influencing variable are available and the remaining values of the time series are modelled, a combination of a non-linear computerized recurrent neural predictive network and a linear error model are employed to produce a prediction with the application of maximum likelihood adaption rules. The computerized recurrent neural network can be trained with the assistance of the real-time recurrent learning rule, and the linear error model is trained with the assistance of the error model adaption rule that is implemented on the basis of forward-backward Kalman equations. This model is utilized in order to predict values of the glucose-insulin metabolism of a diabetes patient.

Abstract: A state vector (SVt) is determined with elements that characterize a financial market (101). Taking into account predetermined evaluation variables, an evaluation (Vt) is determined (102) for the state vector (SVt). In addition, a chronologically following state vector (SVt+1) is determined (103) and evaluated (Vt+1). On the basis of the two evaluations (Vt, Vt+1), weights (wi) of the neural network (NN) are adapted (104) using a reinforcement learning method (&Dgr;wi).

Abstract: A process and an arrangement for conditioning input variables of a neural network are described by the invention. From the input variables of the network, time series are formed and these are subdivided into intervals whose length depends on how far the interval and the measured variables contained therein lie back in the past. In this case, the interval length is selected to be larger the further the interval lies back in the past. By means of convolution using a bell-shaped function, a representative input value for the neural network is obtained from all these measured variables contained in the interval. All the input variables which are obtained in this way are fed to the network simultaneously during training and during operation. A memory is thus realized in a simple way for a forwardly directed neural network. Potential applications include, in particular, chemical processes having very different time constants.

Abstract: In a method for tranining a neural network with the non-deterministic behavior of a technical system, weightings for the neurons of the neural network are set during the training using a cost function. The cost function evaluates a beneficial system behavior of the technical system to be modeled, and thereby intensifies or increases the weighting settings which contribute to the beneficial system behavior, and attenuates or minimizes weightings which produce a non-beneficial behavior. Arbitrary or random disturbances are generated by disturbing the manipulated variable with noise having a known noise distribution, these random disturbances significantly faciliating the mathematical processing of the weightings which are set, because the terms required for that purpose are simplified. The correct weighting setting for the neural network is thus found on the basis of a statistical method and the application of a cost function to the values emitted by the technical system or its model.

Abstract: Optimum values for manipulated variables s.sub.1, . . . ,s.sub.S of a technical system (.phi.) are determined for prescribed operational variables b.sub.1, . . . ,b.sub.B in order to optimize a prescribed target function z=g(y.sub.2. . . ,y.sub.N) of system properties y.sub.2, . . . , y.sub.N where y.sub.i =.phi..sub.i (s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.B). Here, a set of functions f.sub.i (w.sub.1, . . . ,w.sub.W,x.sub.1, . . . ,x.sub.S=B) is used whose parameters w.sub.1, . . . ,w.sub.W are set in such a way that the functions f.sub.i model the system in such a way that the functions f.sub.i approximate the system functions .phi.i as functions of their variables x.sub.1, . . . ,x.sub.S+B in terms of an interval of prescribed magnitude; the manipulated variables s.sub.1, . . . ,s.sub.S are determined by optimizing the function g(f.sub.1 (w.sub.1, . . . ,w.sub.W,s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.B), . . . , f.sub.N (w.sub.1, . . . ,w.sub.W,s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.

Abstract: In a learning method for training a recurrent neural network having a number of inputs and a number of outputs with at least one output being connected via a return line to an input, the return line is separated during training of the neural network, thereby freeing the input connected to the return line for use as an additional input during training, together with the other inputs. The additional input values, which must be estimated or predicted for supply to the thus-produced additional training inputs, are generated by treating each additional input value to be generated as a missing value in the time series of input quantities. Error distribution densities for the additional input values are calculated on the basis of the known values from the time series and their known or predetermined error distribution density, and samples are taken from this error distribution density according to the Monte Carlo method.

Abstract: In a method for supplementing missing data in a time series used as an input to a neural network or for improving noise-infested data supplied to a neural network, error distribution densities for the missing values are calculated on the basis of the known measured values from the time series and their known or predetermined error distribution density, and samples are taken from this error distribution density according to the Monte Carlo method. These each lead to an estimated or predicted value whose average is introduced for the value to be predicted. The method can be employed for the operation as well as for the training of the neural network, and is suitable for use in all known fields of utilization of neural networks.

Abstract: The non-linear filter architecture according to the invention provides a neural network for modelling a non-linear transfer function, there being supplied to the neural network, on the input side, the filter input signals f(n), . . . , f(n-i), . . . , f(n-M), a time index signal i and the values p(n), . . . , p(n-i), . . . , p(n-M) of the parameter vector p. The neural network uses these values to calculate, at each time i, output values which are summed for the M+1 times i=0, . . . , M, as a result of which the filter output function g(n) is formed. The invention can be used for implementing a method for overcoming noise signals in digital signal processing, by using a circuit arrangement or a software system. Specifically, the invention can be used in a method for suppressing cardio-interference in magneto-encephalography. The invention can furthermore be used for overcoming motor noise.