Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Approaches are provided for answering an inquiry of a cognitive distributed network. An approach includes receiving the inquiry at the cognitive distributed network. The approach further includes determining a classification for the inquiry based on natural language of the inquiry. The approach further includes classifying the inquiry as a single question class. The approach further includes determining, by at least one computing device, a type of introspection to be used by the cognitive distributed network on the inquiry. The approach further includes generating an answer to the inquiry based on the determined type of introspection.

Cognitive distributed network

Method, system, and computer program product to archive data objects, by providing an object store and a shadow object store each configured to store one or more data objects, the object store and the shadow object store residing in different storage devices, creating a first object stored in the object store, the first object having at least one attribute, and responsive to receiving a request to archive the first object, transferring the first object to the shadow object store, and creating, in the object store, a reference to a location of the first object in the shadow object store, the first object remaining accessible through the object store and the shadow object store following the transfer.

System for on-line archiving of content in an object store

Automated drive assisting systems, methods, and programs acquire an expected travel route for a vehicle that includes an automated drive section in which automated drive control for the vehicle is permitted, and execute the automated drive control for the vehicle such that the vehicle travels along the expected travel route in the automated drive section which is included in the expected travel route. The systems, methods, and programs make a changeover from the automated drive control to manual drive based on a drive operation by a user in the case where there is a branch point that meets a predetermined condition ahead in a travel direction of the vehicle which travels through the automated drive control in the automated drive section, the changeover being made before the branch point.

Automatic driving assistance system, automatic driving assistance method, and computer program

Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Hyperparameter and network topology selection in network demand forecasting

A system, method and program product for selecting an algorithm and feature set to solve a problem. A perpetual analytics system is disclosed that provides a genetic algorithm for jointly selecting an algorithm and feature set to solve a problem, comprising: an evolutionary computing engine for processing data encoded as chromosomes, wherein each chromosome encodes an algorithm and a feature set; a domain knowledge store that maintains a plurality of algorithms and a plurality of features; a system for applying a generation of chromosomes to a set of data to provide a set of results; and a fitness function for evaluating the set of results to rate a performance of each chromosome in the set of chromosomes; wherein the evolutionary computing engine is adapted to evolve a subset of the set of chromosomes into a new generation of chromosomes.

Choosing pattern recognition algorithms and data features

Choosing pattern recognition algorithms and data features using a genetic algorithm

The field of Biometrics analyses organic signals from people to identify or verify an identity using a combination of physiological, behavioural or cognitive characteristics such as voice, fingerprints, eye color, facial features, iris, handwriting or other characteristics. Large-scale biometric identification systems can benefit from modern optimisation, classification and parallel computation techniques to reduce cost and increase accuracy. This chapter discusses recent and novel developments by the authors in the approaches taken to enable large-scale biometric identification. The authors present an overview of different techniques to perform the tasks of search space reduction, feature selection and parallel processing of biometrics data. Topics covered are: support vector machines and hyperspace transformations for effectively searching extremely large fingerprint databases to identify individuals; evolutionary computing to perform efficient facial feature selection for identification purposes; and cloud and high-performance designs for biometric systems.

Large-Scale Biometric Multimedia Processing

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Papers

Choosing pattern recognition algorithms and data features

Hyperparameter and network topology selection in network demand forecasting

Choosing pattern recognition algorithms and data features using a genetic algorithm

Large-Scale Biometric Multimedia Processing