It seems to me, that above definition of k-folded cross validation algorithm (from Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016) is inconsistent with the common definition of cross - validation. In above algorithm $e$ vector is the vector of loss function calculated for every particular example in the $D$ dataset, and then mean of vector $e$ is the estimation of generalization error. Whereas in standard definition of cross - validation, we calculate test error for each fold and then calculate average of them.

Many driver assistance systems such as Adaptive Cruise Control require the identification of the closest vehicle that is in the host vehicle's path. This entails an assignment of detected vehicles to the host vehicle path or neighboring paths. After reviewing approaches to the estimation of the host vehicle path and lane assignment techniques we introduce two methods that are motivated by the rationale to filter measured data as late in the processing stages as possible in order to avoid delays and other artifacts of intermediate filters. These filters generate discrete posterior probability distributions from which a path or "lane" index is extracted by a median estimator. The relative performance of those methods is illustrated by a ROC using experimental data and labeled ground truth data.

The number of "feature values" is the total number of key-value pairs found, including the small unstable ones, regardless as to whether they are classified as good or bad. Any article with a pv above the arbitrary value pv_threshold 7.1 (see source code) is considered as good. This corresponds to articles having about 1.3 times more traffic than average, since we use a log scale and the average pv is 6.81. The traffic for articles classified as good by the algorithm (pv 8.23) is about 4.2 times above the traffic that an average article receives. Also note that we correctly identify the vast majority of good articles, but this is because we work with small nodes. Finally an article is marked as good if it triggers at least one node marked as good (that is, satisfying the criterion defined in the next sub-section.) Besides pv_threshold, the algorithm uses 12 parameters to identify a usable, stable node classified as good.

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. We can make machines learn to do things! The first time I heard that, it blew my mind. That means that we can program computers to learn things by themselves! The ability of learning is one of the most important aspects of intelligence. Translating that power to machines, sounds like a huge step towards making them more intelligent. And in fact, Machine Learning is the area that is making most of the progress in Artificial Intelligence today; being a trendy topic right now and pushing the possibility to have more intelligent machines.

When speaking about AI (artificial intelligence) and deep learning technologies, the Chinese are not just innovating at rocket speed, but the businesses are rapidly adopting these technologies in real-life business use cases to optimise customer experience and expanding market reach. Let's have a look at how the Chinese banking and financial industries are using facial recognition in business operations governed by stringent security requirements. As the customer repeatedly progresses his banking and financing needs with a bank or financial institutions, why can't the customer apply for a new product or service, from the comfort of his/her couch at home/office? Alipay (an asset of Alibaba's Ant Financial and world's largest mobile payment platform since 2014 Q2), China Merchants Bank (as of 2015, it ranks third of all Chinese companies for net cash), China CITIC Bank (China's seventh-largest lender in terms of total assets), Bank of Jiangsu and Ping An Bank are using facial recognition API to accomplish remote ID verification, either through the convenience of a mobile app, or a self-service kiosk/ATM/VTM. China Merchants Bank's Self-Service ATM equipped with Facial Recognition Technology for ID Verification Some of these case studies are cited to have only 0.001 error rate at 98% True Positive Rate.

It's been three years since "The Streak" came to an end, but The Undertaker's match is still one of the biggest parts of WrestleMania each year. While it hasn't been confirmed that the Deadman will perform at WrestleMania 33, it would be surprising if he wasn't on the card at WWE's signature event of 2017. In the months leading up to WrestleMania, there is always speculation regarding who The Undertaker might face. Following his win over Shane McMahon at WrestleMania 32, The Undertaker's next opponent might not be the WWE superstar that many fans had once thought it would be. Before John Cena underwent surgery in January of last year, he was reportedly supposed to face The Undertaker in front of over 100,000 people at AT&T Stadium.

Following the 2017 Royal Rumble, the WrestleMania 33 match card looks to be falling into place. Brock Lesnar vs. Goldberg has already been made official for the pay-per-view, and a few of the other top matches aren't difficult to predict. There seems to be some uncertainty, however, surrounding the future plans for WWE's biggest full-time performer. With WrestleMania 33 still nearly two months away, Cena's opponent remains unclear. As the current WWE Champion, Cena is in line to face Royal Rumble winner Randy Orton on April 2 in Orlando. That could change at Sunday's PPV when Cena puts his belt on the line against five other wrestlers in the Elimination Chamber.

Sophos (LSE: SOPH), a global leader in network and endpoint security, today announced it has entered into an agreement to acquire Invincea, a visionary provider of next-generation malware protection. Invincea's endpoint security portfolio is designed to detect and prevent unknown malware and sophisticated attacks via its patented deep learning neural-network algorithms. It has been consistently ranked as among the best performing machine learning, signature-less next-generation endpoint technologies in third-party testing and rated highly both for high detection and low false-positive rates. Headquartered in Fairfax, Va., Invincea was founded by chief executive officer Anup Ghosh to address the rapidly growing zero-day security threat from nation states, cyber criminals and rogue actors. Invincea's flagship product X by Invincea uses deep learning neural networks and behavioral monitoring to detect previously unseen malware and stops attacks before damage occurs.

The road to WrestleMania 33 is in full swing, but there are still two pay-per-views on the schedule before the biggest WWE show of 2017. The first one is Elimination Chamber Sunday night, featuring the "SmackDown Live" roster. Elimination Chamber will likely establish multiple WrestleMania title matches, starting with the WWE World Championship match. Bray Wyatt, Baron Corbin, The Miz, AJ Styles and Dean Ambrose will all compete against John Cena for the No. 1 belt on "SmackDown Live." The SmackDown Women's Championship and the SmackDown Tag Team Championships will also be on the line at the PPV in Phoenix.

This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building blocks of the multi-level detection architecture. The detectors identify malicious communication exploiting encrypted URL strings and domains generated by a Domain Generation Algorithm (DGA) which are frequently used in Command and Control (C&C), phishing, and click fraud. Surprisingly, very precise detectors can be built given only a limited amount of information extracted from a single proxy log. This way, the computational requirements of the detectors are kept low which allows for deployment on a wide range of security devices and without depending on traffic context such as DNS logs, Whois records, webpage content, etc. Results on several weeks of live traffic from 100+ companies having 350k+ hosts show correct detection with a precision exceeding 95% of malicious flows, 95% of malicious URLs and 90% of infected hosts. In addition, a comparison with a signature and rule-based solution shows that our system is able to detect significant amount of new threats.