CHAPTER 14 - Representation Generation in an Exploratory Learning System

In domains in which single agent learning is a more natural metaphor for an artifact-embedded agent, Exemplar-Based Learning (EBL) requires significantly large sets of training examples for it to be applicable. Obviously large sets of training examples contradict resource capabilities of artifacts. To make EBL a possibility for these artifacts, sets of training examples must be reduced in size in a way that does not compromise learning performance in order to relieve artifacts' resources (e.g. memory). In this paper, we investigate training sets requirements for artifacts learning and propose a ranking-based Stratified Ordered Selection (SOS) method to scale them down. Contrary to reduction approaches in mainstream learning, this method has been designed with resource constraint nature of artifacts in mind. Artifacts shall use an intermediary which implements SOS to, dynamically and on-demand, retrieve training subsets based on their resource capacities (e.g. memory, CPU). SOS uses a new Level Order (LO) ranking scheme which has been designed to broaden representation of classes of examples, to quicken data retrieval, and to allow for retrieval of subsets of varying sizes while ensuring same or near same learning performance. We present how SOS evaluates on various well known machine learning datasets and how it compares to some of the best performing data reduction approaches.

This paper introduces a method that improves the performance of a problem solver by reformulating its domain theory into one in which functionally relevant features are explicit in the syntax. This method, in contrast to previous reformulation methods, employs sets of training examples to constrain and direct the reformulation process. The use of examples offers two advantages over purely deductive approaches: First, the examples identify the exact part of the domain theory to be reformulated. Second, a proof with examples is much simpler to construct than a general proof because it is fully instantiated. The method exploits the fact that what is relevant to a goal is syntactically explicit in successful solutions to that goal. The method first takes as input a set of training examples that “exercise” an important part of the domain theory and then applies the problem solver to explain the examples in terms of a relevant goal. Next, the set of explanations is “clustered” into cases and then generalized using the induction over explanations method, forming a set of general explanations. Finally, these general explanations are reformulated into new domain theory rules. We illustrate the method in the domain of chess. We reformulate a simple declarative encoding of legal-move to produce a new domain theory that can generate the legal moves in a tenth of the time required by the original theory. We also show how the reformulated theory can more efficiently describe the important knight-fork feature.

Whoso neglects learning in his youth, loses the past and is dead for the future . –Euripides (484 BC – 406 BC), Phrixus, Frag. 927 Learning is the ability of an agent to improve its behavior based on experience. This could mean the following: The range of behaviors is expanded; the agent can do more. The accuracy on tasks is improved; the agent can do things better. The speed is improved; the agent can do things faster. The ability to learn is essential to any intelligent agent. As Euripides pointed, learning involves an agent remembering its past in a way that is useful for its future. This chapter considers supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new input. We show how such learning may be based on one of four possible approaches: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or finding the posterior probability distribution of hypotheses conditioned on the training examples. Chapter 11 goes beyond supervised learning and considers clustering (often called unsupervised learning), learning probabilistic models, and reinforcement learning. Section 14.2 (page 606) considers learning relational representations.

Who so neglects learning in his youth, loses the past and is dead for the future. – Euripides (484 BC – 406 BC), Phrixus, Frag. 927 Learning is the ability of an agent to improve its behavior based on experience. This could mean the following • The range of behaviors is expanded; the agent can do more. • The accuracy on tasks is improved; the agent can do things better. • The speed is improved; the agent can do things faster. The ability to learn is essential to any intelligent agent. As Euripides pointed out, learning involves an agent remembering its past in a way that is useful for its future. This chapter considers the problem of making a prediction as supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new example where only the inputs are given. We explore four approaches to learning: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or (in Section 10.4 (page 512)) predicting based on the posterior probability distribution of hypotheses conditioned on the training examples. Chapter 10 considers learning probabilistic models. Chapter 12 covers reinforcement learning. Section 15.2 (page 701) considers learning relational representations. Learning Issues The following components are part of any learning problem: Task The behavior or task that is being improved Data The experiences that are used to improve performance in the task, usually in the form of a sequence of examples Measure of improvement How the improvement is measured – for example, new skills that were not present initially, increasing accuracy in prediction, or improved speed Consider the agent internals of Figure 2.9 (page 66). The problem of learning is to take in prior knowledge and data (e.g., about the experiences of the agent) and to create an internal representation (the knowledge base) that is used by the agent as it acts. Learning techniques face the following issues: Task Virtually any task for which an agent can get data or experiences can be learned.

We develop a novel framework that aims at automatically adapting previously learned information extraction knowledge from a source Web site to a new unseen target site in the same domain. Two kinds of features related to the text fragments from the Web documents are investigated. The first type of feature is called, a site-invariant feature. These features likely remain unchanged in Web pages from different sites in the same domain. The second type of feature is called a site-dependent feature. These features are different in the Web pages collected from different Web sites, while they are similar in the Web pages originating from the same site. In our framework, we derive the site-invariant features from previously learned extraction knowledge and the items previously collected or extracted from the source Web site. The derived site-invariant features will be exploited to automatically seek a new set of training examples in the new unseen target site. Both the site-dependent features and the site-invariant features of these automatically discovered training examples will be considered in the learning of new information extraction knowledge for the target site. We conducted extensive experiments on a set of real-world Web sites collected from three different domains to demonstrate the performance of our framework. For example, by just providing training examples from one online book catalog Web site, our approach can automatically extract information from ten different book catalog sites achieving an average precision and recall of 71.9% and 84.0% respectively without any further manual intervention.

A mixed-integer programming approach to GRNN parameter estimation

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.

Probability estimation from a given set of training examples is crucial for learning Naive Bayes (NB) Classifiers. For an insufficient number of training examples, the estimation will suffer from the zero-frequency problem which does not allow NB classifiers to classify instances whose conditional probabilities are zero. Laplace-estimate and M-estimate are two common methods which alleviate the zero-frequency problem by adding some fixed terms to the probability estimation to avoid zero conditional probability. A major issue with this type of design is that the fixed terms are pre-specified without considering the uniqueness of the underlying training data. In this paper, we propose an Artificial Immune System (AIS) based self-adaptive probability estimation method, namely AISENB, which uses AIS to automatically and self-adaptively select the optimal terms and values for probability estimation. The unique immune system based evolutionary computation process, including initialization, clone, mutation, and crossover, ensure that AISENB can adjust itself to the data without explicit specification of functional or distributional forms for the underlying model. Experimental results and comparisons on 36 benchmark datasets demonstrate that AISENB significantly outperforms traditional probability estimation based Naive Bayes classification approaches.

When a Deep Neural Network makes a misprediction, it can be challenging for a developer to understand why. While there are many models for interpretability in terms of predictive features, it may be more natural to isolate a small set of training examples that have the greatest influence on the prediction. However, it is often the case that every training example contributes to a prediction in some way but with varying degrees of responsibility. We present Partition Aware Local Model (PALM), which is a tool that learns and summarizes this responsibility structure to aide machine learning debugging. PALM approximates a complex model (e.g., a deep neural network) using a two-part surrogate model: a meta-model that partitions the training data, and a set of sub-models that approximate the patterns within each partition. These sub-models can be arbitrarily complex to capture intricate local patterns. However, the meta-model is constrained to be a decision tree. This way the user can examine the structure of the meta-model, determine whether the rules match intuition, and link problematic test examples to responsible training data efficiently. Queries to PALM are nearly 30x faster than nearest neighbor queries for identifying relevant data, which is a key property for interactive applications.

Sparse matrix vector multiplication (SpMV) is one of the most common operations in scientific and high-performance applications, and is often responsible for the application performance bottleneck. While the sparse matrix representation has a significant impact on the resulting application performance, choosing the right representation typically relies on expert knowledge and trial and error. This paper provides the first comprehensive study on the impact of sparse matrix representations on two emerging many-core architectures: the Intel's Knights Landing (KNL) XeonPhi and the ARM-based FT-2000Plus (FTP). Our large-scale experiments involved over 9,500 distinct profiling runs performed on 956 sparse datasets and five mainstream SpMV representations. We show that the best sparse matrix representation depends on the underlying architecture and the program input. To help developers to choose the optimal matrix representation, we employ machine learning to develop a predictive model. Our model is first trained offline using a set of training examples. The learned model can be used to predict the best matrix representation for any unseen input for a given architecture. We show that our model delivers on average 95% and 91% of the best available performance on KNL and FTP respectively, and it achieves this with no runtime profiling overhead.

Design of multicategory multifeature split decision trees using perceptron learning

It is often expensive to acquire data in real-world data mining applications. Most previous data mining and machine learning research, however, assumes that a fixed set of training examples is given. In this paper, we propose an online cost-sensitive framework that allows a learner to dynamically acquire examples as it learns, and to decide the ideal number of examples needed to minimize the total cost. We also propose a new strategy for Partial Example Acquisition (PAS), in which the learner can acquire examples with a subset of attribute values to reduce the data acquisition cost. Experiments on UCI datasets show that the new PAS strategy is an effective method in reducing the total cost for data acquisition.

We describe a method of pairwise 3D surface correspondence for the automated generation of landmarks on a set of examples from a class of shape. We show how the pairwise corresponder can be used in an extension of an existing framework for establishing dense correspondences between a set of training examples to build a 3D statistical model. The framework relies upon additional algorithms for the production of surface paths between vertices on a polyhedral mesh, and these are described. An example statistical model is shown for the left lateral ventricle of the brain.

Active reducing classification error for CAD systems