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

Embodiments of the present invention provide techniques for retrieving electronic documents based upon images captured using an image capture device. One or more images captured by a user using an image capture device are used to search a set of documents to retrieve one or more documents that match the search query. The one or more documents retrieved by the search may then be provided to the user or some other recipient.

Techniques for retrieving documents using an image capture device

A M ixed M edia R eality (MMR) system and associated techniques are disclosed. The MMR system provides mechanisms for forming a mixed media document that includes media of at least two types (e.g., printed paper as a first medium and digital content and/or web link as a second medium). In one particular embodiment, the MMR system includes an action processor and method, and MMR documents with an associated action. The MMR document structure is particularly advantageous because the ability to specify different actions for different MMR documents, combined with the ability to create any number of MMR documents for a particular location on any media, allows the MMR architecture to serve as a universal trigger or initiator for additional processing. In other words, addition processing or actions can be triggered or initiated based on MMR recognition. The action processor receives the output of the MMR recognition process which yields an MMR document including at least one action. The action processor executes that action which includes various commands to the MMR system or other systems coupled to the MMR system. The MMR system architecture is advantageous because an action can be executed by pointing the capture device at a block of text, and the action is performed. Example actions include retrieving the text in electronic form to the capture device, retrieving the specification for the action, inserting data to a MMR document, transferring data between documents, purchasing items, authoring actions or reviewing historical information about actions. The MMR system includes a variety of user applications (one or more actions) initiated by the MMR recognition of a text patch such as information retrieval for a travel guide book, stock listings or advertisements; information capture such as recording content from a conference, recording and storing multimedia associated with the document, capturing information for a calendar and on the fly authoring; purchasing media files for storage on any part of an MMR document.

Triggering applications based on a captured text in a mixed media environment

A Mixed Media Reality (MMR) system and associated techniques are disclosed. The MMR system of the present invention provides mechanisms for forming a mixed media document that includes media of at least two types, such as printed paper as a first medium and a digital photograph, digital movie, digital audio file, or web link as a second medium. Furthermore, the MMR system of the present invention facilitates business methods that take advantage of the combination of a portable electronic device, such as a cellular camera phone, and a paper document.

System And Methods For Creation And Use Of A Mixed Media Environment

A M ixed M edia R eality (MMR) system and associated techniques are disclosed. The MMR system provides mechanisms for forming a mixed media document that includes media of at least two types (e.g., printed paper as a first medium and digital content and/or web link as a second medium). In one particular embodiment, the MMR system includes a method, system, and computer program product for transforming characters corresponding to a hotspot in a document. An electronic document to be printed is parsed for a mark indicating a beginning point for a hotspot, a transformation rule is a applied to a portion of the electronic document between the mark indicating the beginning point for the hotspot and a mark indicating an ending point for the hotspot, and the transformed electronic document is rendered.

Visibly-perceptible hot spots in documents

One of the aims of the EU project COLLATE is to design and implement a Web-based collaboratory for archives, scientists and end-users working with digitized cultural material. Since the originals of such a material are often unique and scattered in various archives, severe problems arise for their wide fruition. A solution would be to develop intelligent document processing tools that automatically transform printed documents into a Web-accessible form such as XML. Here, we propose the use of a document processing system, WISDOM++, which uses heavily machine learning techniques in order to perform such a task, and report promising results obtained in preliminary experiments.

Machine learning methods for automatically processing historical documents: from paper acquisition to XML transformation

First-Order Logic formulae are a powerful representation formalism characterized by the use of relations, that cause serious computational problems due to the phenomenon of indeterminacy (various portions of one description are possibly mapped in different ways onto another description). Being able to identify the correct corresponding parts of two descriptions would help to tackle the problem: hence, the need for a framework for the comparison and similarity assessment. This could have many applications in Artificial Intelligence: guiding subsumption procedures and theory revision systems, implementing flexible matching, supporting instance-based learning and conceptual clustering. Unfortunately, few works on this subject are available in the literature. This paper focuses on Horn clauses, which are the basis for the Logic Programming paradigm, and proposes a novel similarity formula and evaluation criteria for identifying the descriptions components that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in the above tasks.

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A General Similarity Framework for Horn Clause Logic

Scientific conference management involves many complex and multi-faceted activities, which would make highly desirable for the organizing people to have a Web-based management system that makes some of them a little easier to carry out. One of such activities is the assignment of submitted papers to suitable reviewers, involving the authors, the reviewers and the conference chair. Authors that submit the papers usually must fill a form with paper title, abstract and a set of conference topics that fit their submission subject. Reviewers are required to register and declare their expertise on the conference topics (among other things). Finally, the conference chair has to carry out the review assignment taking into account the information provided by both the authors (about their paper) and the reviewers (about their competencies). Thus, all this subtasks needed for the assignment are currently carried out manually by the actors. While this can be just boring in the case of authors and reviewers, in case of conference chair the task is also very complex and time-consuming.

In this paper we propose the exploitation of intelligent techniques to automatically extract paper topics from their title and abstract, and the expertise of the reviewers from the titles of their publications available on the Internet. Successively, such a knowledge is exploited by an expert system able to automatically perform the assignments. The proposed methods were evaluated on a real conference dataset obtaining good results when compared to handmade ones, both in terms of quality and user-satisfaction of the assignments, and for reduction in execution time with respect to the case of humans performing the same process.

/pdf/automatic-topics-identification-for-reviewer-assignment-515g9k6s7b.pdf

Automatic topics identification for reviewer assignment

Real-world tasks often involve a continuous flow of new information that affects the learned theory, a situation that classical batch (one-step) learning systems are hardly suitable to handle. On the contrary, incremental (also called "on-line") techniques are able to deal with such a situation by exploiting refinement operators. In many cases deep knowledge about the world is not available: Either incomplete information is available at the time of initial theory generation, or the nature of the concepts evolves dynamically. The latter situation is the most difficult to handle since time evolution needs to be considered. This work presents a new approach to learning in presence of concept drift, and in particular a special version of the incremental system INTHELEX purposely designed to implement such a technique. Its behavior in this context has been checked and analyzed by running it on two different datasets.

Incremental learning and concept drift in INTHELEX

Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a suitable background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation includes numerical information, such as single values or intervals, for which simple syntactic matching is not sufficient.

This work proposes an extension of an existing framework for similarity assessment between First-Order Logic Horn clauses, that is able to handle numeric information in the descriptions.

The viability of the solution is demonstrated on sample problems.

http://www.di.uniba.it/%7Endm/pubs/ferilli09aiia.pdf

N. Di Mauro

Papers

Machine learning methods for automatically processing historical documents: from paper acquisition to XML transformation

A General Similarity Framework for Horn Clause Logic

Automatic topics identification for reviewer assignment

Incremental learning and concept drift in INTHELEX

Plugging Numeric Similarity in First-Order Logic Horn Clauses Comparison