Pattern Recognition


Machine learning and the shipping markets. #OOTT

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There's a lot of interest around machine learning and finding patterns in data. Machine learning has taken Gary Kasparov, once at the peak of professional chess, and turned him into an author and political opponent of Putin (not exactly bright career paths). There appears to be some pretty low hanging fruit at the moment, and Amazon web services and other providers offer low cost access to processing capabilities needed. I think the sticking point for finding useful information using any of these methods of analysis will be the depth of understanding needed in trade flows to comprehend which patterns are important and the explanations for others.


Co-Clustering Can Provide Industrial Data Pattern Discovery

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Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Collaborative information filtering applications such as movie recommender systems co-cluster the accumulated movie rating provided by viewers and the movies they have watched. Using this information, the viewer is recommended other movies by classifying the rating he/she provided to a viewer ratings-movies watched cluster. An entry Cij of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix.


An introduction to frequent pattern mining - The Data Mining Blog

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Pattern mining algorithms can be applied on various types of data such as transaction databases, sequence databases, streams, strings, spatial data, graphs, etc. Pattern mining algorithms can be designed to discover various types of patterns: subgraphs, associations, indirect associations, trends, periodic patterns, sequential rules, lattices, sequential patterns, high-utility patterns, etc. The Apriori algorithm has given rise to multiple algorithms that address the same problem or variations of this problem such as to (1) incrementally discover frequent itemsets and associations, (2) to discover frequent subgraphs from a set of graphs, (3) to discover subsequences common to several sequences, etc. If you want to continue reading on this topic, you may read my survey on sequential pattern mining, which gives a good introduction to the topic of discovering frequent patterns in sequences (sequential patterns).


How Machine Learning Will Force Marketing to Evolve (Whether We Like it or Not)

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It's an artificial intelligence engine that uses pattern matching at scale to process millions of search queries daily. Instead, Tommy Levi, the Director of Data Science at Unbounce (who just so happens to also possess a PhD in Theoretical Physics), had this to say in The Split (yes, I'm quoting Unbounce who's quoting Tommy Levi… how's that for some journalism?! It delivers this through a combination of over 50-sub equations that blend (a) heuristic analysis, (b) rapid experiments, (c) conversion research and data, (d) video conversion and engagement data, (e) academic studies, and (f) tools and frameworks from the industry's best and brightest. They provide predictable analysis, specific to your website based on rapid-fire pattern matching, that suggests what to test and why.


5 Simple Ways To Optimize Your Chatbot (HowTo) - The Ape Machine

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Meanwhile you can use pattern matching on the type of tags, frequency of tags, and order of tags to do relatively advanced pattern matching, and get pretty good results, as long as there are not too many patterns flying around in the model. This is by no means the fault of the user, or even the fault of the designer of the technology, it is just because there are no real world "generic" solutions to be found when it comes to a group as large and diverse as the world's population. The time is coming where chatbots will make huge advances, and you need to be ready to migrate with the times. You do not want to be too tightly coupled to a service that will eventually charge you as much or more for a general (non tailored) solution, as you could spend hiring your own machine learning experts to create you the fitted technology your company deserves!


Artificial Intelligence in Healthcare: Medical Ethics and the Machine Revolution

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While this theme may be premature, the concern of teaching ethics and valuing human life is a relevant question for machine learning, especially in the realm of healthcare. Creating "laws" or "rules" for ethics in artificial intelligence as Elon Musk calls for is difficult in that ethical bounds are difficult to teach machines. Many companies have done extensive work in training systems that will be working with patients to learn what words mean and common patterns within patient care. When a patient asks about their symptoms they get clinically relevant information paired with their symptoms even if that patient uses non-medical language in describing their chief complaint.


Artificial Intelligence in Healthcare: Medical Ethics and the Machine Revolution

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While this theme may be premature, the concern of teaching ethics and valuing human life is a relevant question for machine learning, especially in the realm of healthcare. Creating "laws" or "rules" for ethics in artificial intelligence as Elon Musk calls for is difficult in that ethical bounds are difficult to teach machines. Many companies have done extensive work in training systems that will be working with patients to learn what words mean and common patterns within patient care. When a patient asks about their symptoms they get clinically relevant information paired with their symptoms even if that patient uses non-medical language in describing their chief complaint.


AI's Future Is In the Cloud, But Why Are Fiber Optic Networks Vital?

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In the financial services sector, trading and risk management are two areas that are benefitting from advancements in deep learning and pattern recognition. Healthcare is deploying image recognition and machine learning techniques to enhance neo-natal care and cancer treatment. In fact, the network is the only entity that interacts with all elements of the cloud -- the cloud data centers, services and applications -- and is ideally positioned to enhance both cloud and machine learning adoption. Advanced fiber-optic networks and transport services will therefore be required to enable cloud services with the necessary availability, security, performance and scalability to drive increased adoption of AI and machine learning applications.


AI's Future Is In the Cloud, But Why Are Fiber Optic Networks Vital? - Telecom Newsroom

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If you own an iPhone and have ever asked Siri to find you the nearest Brazilian steakhouse, you've used a machine learning program. In the financial services sector, trading and risk management are two areas that are benefitting from advancements in deep learning and pattern recognition. Healthcare is deploying image recognition and machine learning techniques to enhance neo-natal care and cancer treatment. And civil engineers and city planners are leveraging deep learning programs to predict traffic conditions and automobile accidents, as well as the structural response of multi-story buildings during earthquakes.


Here's how RankBrain does (and doesn't) impact SEO

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Using previously processed data in vectors and shards, RankBrain looks to make an intelligent guess based on similar queries, and similar meanings. ARL (association rule learning) is a method of machine learning for discovering relationships between variables in large databases using predetermined measures of interestingness. Taking queries that trigger Venice results and the map pack out of the equation, some queries may demand high velocities of fresh content, shorter content, longer content, lots of links… The new weighting model that RankBrain presents means that there will need to be deviations from the standard best practice. We know from Google's search quality evaluation guidelines that Google considers main and supplemental content when ranking a page; this extends to pages within a URL subdirectory and pages linked to from the main content.