CORRELATION


Naive Principal Component Analysis in R

@machinelearnbot

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. Identify number of components (aka factors) In this stage, principal components (formally called'factors' at this stage) are identified among the set of variables. Cumulative var: variance added consecutively up to the last component. Cumulative proportion: the actually explained variance added consecutively up to the last component.


IT pros get a handle on machine learning and big data

#artificialintelligence

Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed). For example, overall accuracy is usually defined as the number of instances that were truly labeled (true positives plus true negatives) divided by the total instances. If you want to know how many of the actual positive instances you are identifying, sensitivity (or recall) is the number of true positives found divided by the total number of actual positives (true positives plus false negatives). And often precision is important too, which is the number of true positives divided by all items labeled positive (true positives plus false positives).


Deep learning vs. machine learning: The difference starts with data

#artificialintelligence

The answer to the question of what makes deep learning different from traditional machine learning may have a lot... You forgot to provide an Email Address. For example, he pointed out that conventional machine learning algorithms often plateau on analytics performance after processing a certain amount of data. Comcast is also applying computer vision, audio analysis and closed-caption text analysis to video content to break movies and TV shows into "chapters" and automatically generate natural-language summaries for each chapter. Essa said that forward-thinking enterprises will find ways to leverage deep learning to develop new business models, while traditional machine learning is essentially relegated to helping businesses perform existing operations more efficiently.


The Last Mile of IoT: Artificial Intelligence (AI) - OpenMind

#artificialintelligence

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI (Artificial Intelligence) as the last mile of IoT. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines" In an IoT situation, AI can help companies take the billions of data points they have and boil them down to what's really meaningful. The general premise is the same as in the retail applications – review and analyze the data you've collected to find patterns or similarities that can be learned from, so that better decisions can be made. The data collected, combined with AI, makes life easier with intelligent automation, predictive analytics and proactive intervention.


SESSION 3 PAPER 5 LEARNING MACHINES

Classics (Collection 2)

Recent activities have swung away from biology, but this will be remedied. THE application of learning machines to process control is discussed. Three approaches to the design of learning machines are shown to have more in common than is immediately apparent. These are (1) based on the use of conditional probabilities, (2) suggested by the idea that biological learning is due to facilitation of synapses and (3) based on existing statistical theory dealing with the optimisation of operating conditions. Although the application of logical-type machines to process control involves formidable complexity, design principles are evolved here for a learning machine which deals with quantitative signal and depends for its operation on the computation of correlation coefficients.


SESSION 3 PAPER 4 TWO THEOREMS OF STATISTICAL SEPARABILIiY IN THE PERCEPTRON

Classics (Collection 2)

Frank Rosenblatt, born in New Rochelle, New York, U.S.A., July 11, 1928, graduated from Cornell University in 1950, and received a PhD degree in psychology, from the same university, in 1956. He was engaged in research on schizophrenia, as a Fellow of the U.S. Public Health Service, 1951-1953. He has made contributions to techniques of multivariate analysis, psychopathology, information processing and control systems, and physiological brain models. He is currently a Research Psychologist at the Cornell Aeronautical Laboratory, Inc., in Buffalo, New York, where he Is Project Engineer responsible for Project PARA (Perceiving and Recognizing Automaton). FRANK ROSENBLATT SUMMARY A THEORETICAL brain model, the perceptron, has been developed at the Cornell Aeronautical Laboratory, In Buffalo, New York.


A Reprint from

Classics (Collection 2)

THIRD LONDON SYMPOSIUM Papers read at a Symposium on'Information Theory' held at the Royal Institution, London, September 12th to 16th 1955 Published by BUTTERWORTHS SCIENTIFIC PUBLICATIONS 88 KINGSWAY, LONDON, W.C.2 33 PATTERN RECOGNITION AND LEARNING* Massachusetts Institute of Technology, U.S.A. MANY psychologists studying learning have assumed that the subject--rat, dog, or graduate student--invariably knows what the stimulus is. They have not concerned themselves with how a dog knows that it is the bell ringing which is the stimulus to jump over a fence. A bell ringing never gives the same set of nervous impulses into the brain twice (of course the argument would still apply even if it did); why then should the dog classify all cases of bell ringing into one category--'stimulus'? There is then the further question of how this category is more or less quickly'associated' with a response: the point is that the stimulus is not a priori considered a significant entity by the subject. In designing programmes for computers to imitate conditioned reflexes, for instance, we have found that the real problem was to identify the stimulus.


Developing Hierarchical Representations for Protein Structures: An Incremental Approach

Classics (Collection 2)

The protein folding problem has been attacked from many directions. One set of approaches tries to find out correlations between short subsequences of proteins and the structures they form, using empirical information from crystallographic databases. AI research has repeatedly demonstrated the importance of representation in making these kinds of inferences. In this chapter, we describe an attempt to find a good representation for protein substructure. Our goal is to represent protein structures in such a way that they can, on the hand, reflect the enormous complexity and variety of different protein structures, and yet on the other hand facilitate the identification of similar substructures across different proteins.