The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. "This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers."

Austria based ANYLINE, the leading OCR Optical Character Recognition) technology provider for mobile devices, is solely focused on fast and accurate text recognition. Whether reading text across a room, a license plate on a car, or a gas meter in a manufacturing plant, the ANYLINE technology is able to deliver a fast and robust alternative to inputting data via voice recognition, typing, or button scrolling. This type of data import is still difficult to accomplish, as it requires a higher processor power and camera resolution. ANYLINE will now leverage the ... Read more

If you've looked into buying smart devices for your home, you've probably seen the "Works with Nest" badge printed on the outside of one of the boxes, considering how many devices are a part of the service. Alphabet has been pushing to make its Nest thermostat the center of everyone's home by getting IoT manufacturers into the program. Thanks to Alphabet's research in machine learning and pattern recognition, the Works with Nest program has gained extra smarts, allowing your other devices to hook into, and react to, more events. Benefitting most from the newfound capabilities are the Nest cameras, including the new Nest Cam Outdoor. Thanks to Alphabet being able to train its image processing on the millions of photos uploaded by Google Photos' 200 million users, the cameras have gained the ability to recognize if the movement it sees in frame is an actual person or something like a car driving by.

Neurensic, a Chicago-based regtech artificial intelligence (AI) startup, has announced the release of its new SCORE surveillance platform, the trading industry's first compliance solution powered by a cloud-based machine learning architecture which is able to identify complex patterns of trading behavior on a massive scale, across multiple markets in near real time. The FM London Summit is almost here. The development of the new SCORE platform was led by David Widerhorn and Neurensic's CTO, Dr Cliff Click, the inventor of the H2O artificial intelligence framework, the world's fastest distributed machine learning architecture. SCORE combines high-speed, big data processing power with self-adaptive pattern recognition technology, providing firms with a continuous assessment of the compliance risk associated with complex trading behaviours. The firm's recently completed beta release provided clients with surveillance technology for regulators, proprietary trading firms and futures commission merchants, culminating in engagements with larger institutional customers, including broker-dealers and global banks.

DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects. Because of the rapid progress in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations.

DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. This Smart News Release features multimedia. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance.

Following on from an earlier story, where I introduced some common patterns used to build chat bots, we're now going to look at building some of those patterns using IBM Watson. If you haven't used the Watson Conversation service before, you may want to read about the basics of building a bot with Watson in "Getting Chatty with IBM Watson". One of the things I talked about was providing guidance at the beginning of the chat. To provide this before the user says anything you can add a "conversation_start" condition to a node. You can add more conditions to "conversation_start" nodes if you want to have different introductions depending on some external factor, e.g. from your app you could pass in the time of day in the context, and then say "good morning", "good afternoon" or "good evening" depending on that value.

KARIYA, Japan & TOKYO--(BUSINESS WIRE)--DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects.

New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers. In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data. Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.

For all of the good that machine learning and artificial intelligence promise, what happens if either machine learning or AI becomes the next hot marketing buzzword in tech and the business sector? Picture AI falling helplessly into the morass that sucked in "big data," "smart" anything, and "Internet of" you name it. That rumble that you would feel might be Alan Turing fidgeting in his grave. I can hear some marketing newbie now, "Well, our product is made of plastic. And, we think it's really intelligent. For more than 60 years hundreds of very bright and accomplished computer scientists, from Turing to today's doctoral students, have researched and debated what AI is, and what it isn't. At what point is a computer actually thinking? Then, we have machine learning as a subset of or precurser to AI. Feed a neural network with enough examples -- such as text and images -- and it advances to the point where it can translate English into another language, recognize faces of people, or identify the most successful treatments for diseases. I suppose AI is destined to be cast into Buzzword Hades once everyone from that marketing newbie to the CEO desperate for something innovative hear more about the real-world successes of AI and machine learning. Memos and meetings will be punctuated with shouts of, "We need to be doing that." We already are seeing examples of niche applications utilizing techniques such as image recognition in anti-terrorism initiatives and pattern recognition in cybersecurity. Applications in the commercial space seem to be ready to pop up in the public view. A Forbes article cites three industry sectors -- healthcare, finance, and insurance -- as prime candidates for AI and machine learning applications. The article notes, "Sequencing of individual genomes and then comparing them to a vast database will allow doctors -- and/or AI bots -- to predict the probability that you will contract a particular disease and the best ways to treat those diseases when they appear.