Bishop 1995 Artificial Neural Networks

Identifying Oil Spills
artificial neural networks

Identifying Oil Spills using Self Organizing Maps and multi-spectral signatures. In our prototype studies we have found Neural Network (NN) Self Organizing Maps (SOM) to do a remarkable job of not just classifying an oil spill but also quantifying the severity. Neural networks are biologically inspired machine learning algorithms that have been trained to perform complex functions in various fields, including pattern recognition, identification, classification, speech, vision, and control systems.  Today neural networks can be trained to solve problems that are difficult for conventional computers or human beings. Neural networks are non-linear, multivariate, non-parametric learning algorithms inspired by biological nervous systems and are composed of simple elements operating in parallel (Bishop 1995; 1998; Haykin 1999; 2001). As in nature, the network function is determined largely by the connections between elements.

A self-organizing map (SOM) is a type of NN that is trained using unsupervised learning to produce a low dimensional discretized representation of the input space of the training samples, called a map. The map seeks to preserve the topological properties of the input space. This makes SOM useful for visualizing low-dimensional views of high-dimensional data. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen (Kohonen 1984; 2001), and is sometimes called a Kohonen map. Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples, in our case a suite of satellite products that will be used to characterize the ocean surface. Once the network is trained, mapping automatically classifies a new input vector. SOMs have already been used to great effect for a large variety of earth and space science applications. This includes the analyses of remote sensing spectral images, for example in the classification of different geological regions (Merenyi et al. 1990; Merenyi et al. 1996a; Merenyi et al. 1996b; Merenyi et al. 1997; Seiffert and Jain 2001; Villmann et al. 2003; Merenyi et al. 2007). Given this success by others, and our own recent success in using them for both the accurate delineation of dust sources for the Navy and the classification of ecosystems in the Gulf of Mexico (in press) it is timely to apply SOM for our need to delineate oil spill locations. The inputs to the SOM are a suite of satellite products from a suite of instruments including synthetic aperture radar (SAR) and the visible and near infrared water leaving radiances and absorbances, and their derived products. As an example, the figure above in panel (a) shows a picture of oil slick as seen from space by NASA’s Terra satellite on May 24, 2010. Panel (b) shows our prototype SOM classification for the same day. It can be seen that the yellow and red classes correspond to the oil slick, with the severest regions highlighted in red.

Source: MINTS

You might also like:

Chapter 1: A Look Back at the Founding of PatientsLikeMe
Chapter 1: A Look Back at the Founding of PatientsLikeMe
Why did a Social Network CTO choose the Neo4j Graph Database?
Why did a Social Network CTO choose the Neo4j Graph Database?
Heller Lecture - Jean-Pierre Changeux
Heller Lecture - Jean-Pierre Changeux

A Pascal's Wager you should seriously consider

2010-07-05 06:09:35 by magicthighs


With the stock market lurching again, plenty of investors are nervous, and some are downright bearish. Then there’s Robert Prechter, the market forecaster and social theorist, who is in another league entirely.
Prechter is convinced we have entered a market decline of staggering proportions – perhaps the biggest of the last 300 years.
In a series of phone conversations and e-mail exchanges last week, he said that no other forecaster was likely to accept his reasoning, which is based on his version of the Elliott Wave theory – a technical approach to market analysis that he embraces with evangelical fervor.
Originating in the writings of Ralph Nelson Elliott, an obscure accountant who found repetitive patterns, or "fractals," in the stock market of the 1930s and...

I truly enjoy Nat Geo magazine's secular stance

2008-12-03 12:23:43 by Snakebyte_XX

For example, there's a story about King Herod in the December issue that goes into great detail about his rein.
It's distinguished by passages such as this one:
Yet today he is best known as the sly and murderous monarch of Matthew's Gospel, who slaughtered every male infant in Bethlehem in an unsuccessful attempt to kill the newborn Jesus, the prophesied King of the Jews. During the Middle Ages he became an image of the Antichrist: Illuminated manuscripts and Gothic gargoyles show him tearing his beard in mad fury and brandishing his sword at the luckless infants, with Satan whispering in his ear. Herod is almost certainly innocent of this crime, of which there is no report apart from Matthew's account.

and this one:
...

And for good reason

2007-12-16 20:40:10 by spudmuck

The other side recycles the "models-are-inaccurate" argument because it they remain the only evidence offered that humans are responsible for any observed shift in the climate. Without those models there is every indication that what we are experiencing is a normal, natural occurance and any influence humans have is minimal at worst. Those models are the GW camps stock in trade. Without those models they have nothing, or at best very little.
As the primary evidence offered for the theory any critical examination will focus on those models.
As far as credentials go it seems to me the pro-GW camp play pretty fast and loose with this as well. There are many contributors to the IPCC reports whose primary speciality is not climateology. And many that would not qualify as...

AWEX Merino Cardings Price Guide drop sharply  — Fibre2fashion.com

... in commodity prices; and in the transfer of investor funds from Australia to the “safe havens” of US Treasury bonds, the Swiss Franc and the Yen. The transfer of funds from Australia results in a lower Australian exchange rate with the United States.

Inflation expected to fall - RBNZ survey  — TVNZ
They're picking the Australian exchange rate to be 79 cents by the end of March next year. The kiwi recently traded at 76.48 US cents and 77.30 Australian cents. The Reserve Bank surveyed 71 firms out of a sample of 118, and was conducted by Nielsen.

Plume The Neatest Little Guide to Stock Market Investing, 2010 Edition
Book (Plume)
FT Press Investing with Volume Analysis: Identify, Follow, and Profit from Trends
Book (FT Press)
McGraw-Hill Best Practices for Equity Research Analysts: Essentials for Buy-Side and Sell-Side Analysts
Book (McGraw-Hill)

Related posts:

  1. Origin of Artificial Neural Networks
  2. Usage of Artificial Neural Networks
  3. Seminar Topics in Artificial Neural Networks
  4. Models of artificial neural network