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

An Introduction to MultiAgent Systems.

http://www.eis.mdx.ac.uk/staffpages/juanaugusto/CookAJ2009.pdf

Review: Ambient intelligence: Technologies, applications, and opportunities

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading molecular biology of the gene is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

Molecular Biology of the Gene.

The recent development in learning deep representations has demonstrated its wide applications in traditional vision tasks like classification and detection. However, there has been little investigation on how we could build up a deep learning framework in a weakly supervised setting. In this paper, we attempt to model deep learning in a weakly supervised learning (multiple instance learning) framework. In our setting, each image follows a dual multi-instance assumption, where its object proposals and possible text annotations can be regarded as two instance sets. We thus design effective systems to exploit the MIL property with deep learning strategies from the two ends; we also try to jointly learn the relationship between object and annotation proposals. We conduct extensive experiments and prove that our weakly supervised deep learning framework not only achieves convincing performance in vision tasks including classification and image annotation, but also extracts reasonable region-keyword pairs with little supervision, on both widely used benchmarks like PASCAL VOC and MIT Indoor Scene 67, and also a dataset for image-and patch-level annotations.

/pdf/deep-multiple-instance-learning-for-image-classification-and-186npdww6m.pdf

Deep multiple instance learning for image classification and auto-annotation

Symbolic knowledge extraction from trained neural networks: a sound approach

Learning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features.

/pdf/speaker-recognition-with-hybrid-features-from-a-deep-belief-2cnbgcnfh2.pdf

Speaker recognition with hybrid features from a deep belief network

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a difficult task. However, such integration may lead to a better understanding, use and construction of more realistic models. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable for online learning and reasoning. Currently, controlled environments like training simulators do not effectively integrate learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal relations with the data, making it impossible to represent such relationships by hand. We introduce a novel cognitive agent model and architecture for online learning and reasoning that seeks to effectively represent, learn and reason in complex training environments. The agent architecture of the model combines neural learning with symbolic knowledge representation. It is capable of learning new hypotheses from observed data, and infer new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model. The validation of the model on real-time simulations and the results presented here indicate the promise of the approach when performing online learning and reasoning in real-world scenarios, with possible applications in a range of areas.

A neural-symbolic cognitive agent for online learning and reasoning

The development of requirements specifications inevitably involves modification and evolution. To support modification while preserving particular requirements goals and properties, the use of a cycle composed of two phases: analysis and revision is proposed. In the analysis phase, a desirable property of the system is checked against a partial specification. Should the property be violated, diagnostic information is provided. In the revision phase, the diagnostic information is used to help modify the specification in such a way that the new specification no longer violates the original property. An instance of the above analysis–revision cycle that combines new techniques of logical abduction and inductive learning to analyse and revise specifications, respectively is investigated. More specifically, given an (event-based) system description and a system property, abductive reasoning is applied in refutation mode to verify whether the description satisfies the property and, if it does not, identify diagnostic information in the form of a set of examples of property violation. These (counter) examples are then used to generate a corresponding set of examples of system behaviours that should be covered by the system description. Finally, such examples are used as training examples for inductive learning, changing the system description in order to resolve the property violation. This is accomplished with the use of the connectionist inductive learning and logic programming system—a hybrid system based on neural networks and the backpropagation learning algorithm. A case study of an automobile cruise control system illustrates the approach and provides some early validation of its capabilities.

/pdf/combining-abductive-reasoning-and-inductive-learning-to-lc5xe9g7bd.pdf

A.S. d'Avila Garcez

Papers

Symbolic knowledge extraction from trained neural networks: a sound approach

Speaker recognition with hybrid features from a deep belief network

A neural-symbolic cognitive agent for online learning and reasoning

Combining abductive reasoning and inductive learning to evolve requirements specifications

Proceedings of 16th European Conference on Artificial Intelligence, ECAI 2004