Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Attention and Effort

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N £ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensiona l image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position an$ image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configurations to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimensional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but instead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its particular category. But it is only with rare exceptions that an image fails to be rapidly and readily classified, either as an instance of a familiar object category or as an instance that cannot be so classified (itself a form of classification).

/pdf/recognition-by-components-a-theory-of-human-image-2d038hmxno.pdf

Recognition-by-Components: A Theory of Human Image Understanding.

/pdf/weird-languages-have-misled-us-too-comment-on-henrich-et-al-57dmcdjt24.pdf

WEIRD languages have misled us, too [Comment on Henrich et al.]

Parts of recognition (A)

Parts of recognition

1. Introduction D. C. Knill, D. Kersten and A. Yuille 2. Pattern theory: a unifying perspective D. Mumford 3. Modal structure and reliable inference A. Jepson, W. Richards and D. C. Knill 4. Priors, preferences and categorical percepts W. Richards, A. Jepson and J. Feldman 5. Bayesian decision theory and psychophysics A. L. Yuille and H. H. Bulthoff 6. Observer theory, Bayes theory, and psychophysics B. M. Bennett, D. D. Hoffman, C. Prakash and S. N. Richman 7. Implications of a Bayesian formulation D. C. Knill, D. Kersten and P. Mamassian 8. Shape from texture: ideal observers and human psychophysics A. Blake, H. H. Bulthoff and D. Sheinberg 9. A computational theory for binocular stereopsis P. N. Belhumeur 10. The generic viewpoint assumption in a Bayesian framework W. T. Freeman 11. Experiencing and perceiving visual surfaces K. Nakayama and S. Shimojo 12. The perception of shading and reflectance E. H. Adelson and A. P. Pentland 13. Banishing the Homunculus H. Barlow.

Perception as Bayesian Inference

Psychophysical tests reveal three classes of wide-field disparity detectors in man, responding respectively to crossed (near), uncrossed (far), and zero disparities. The probability of lacking one of these classes of detectors is about 30% which means that 2.7% of the population possess no wide-field stereopsis in one hemisphere. This small percentage corresponds to the probability of squint among adults, suggesting that fusional mechanisms might be disrupted when stereopsis is absent in one hemisphere.

/pdf/stereopsis-and-stereoblindness-1ovzpy2do1.pdf

Stereopsis and Stereoblindness

Normal or complete stereoscopic depth perception is based upon at least two and probably three mechanisms. These mechanisms may be isolated by studying depth judgments made by stereoanomalous individuals who are unable to discriminate disparities over wide ranges of disparity. The nature of the reductions observed among these stereoanomalous observers suggests that at least three ranges of the disparity are sampled in order to create three different pools of binocular activity. The pools correspond roughly to crossed, near-zero, and uncrossed disparities.

Whitman Richards

Papers

Parts of recognition (A)

Parts of recognition

Perception as Bayesian Inference

Stereopsis and Stereoblindness

Anomalous Stereoscopic Depth Perception