Small sample learning during multimedia retrieval using BiasMap

For generalization to be functional, it must occur with a precision that results in acquired responses occurring under appropriate, nontrained conditions, and acquired responses not occurring under inappropriate, nontrained conditions. This study examines the effect of differing types of negative teaching examples on the precision with which generalized grocery item selection is learned. Within a split-multiple baseline design, six young adults identified as mildly, moderately, or severely mentally retarded were trained to select or to reject grocery items using picture cards as cues. The dependent variables were correct selection of 10 trained “positive” grocery items and the correct rejection of 20 nontrained “negative” grocery items in a nontrained grocery store. Participants were trained in a grocery store to select 10 positive grocery examples matching their picture cards and to reject either (a) a set of negative examples that were maximally different from the positive examples, or (b) a set of negative examples that were minimally different from the positive examples. Both training sets resulted in participants correctly selecting the 10 positive items in a nontrained store. Training with the “minimally different” negative examples was functionally related to improved rejection of nontrained negative items in the nontrained store. The implications of teaching with minimally different, negative examples are discussed.

Amongst all the methods of machine learning, learning from examples is considered as a key to acquire knowledge automatically. Learning from examples is to obtain a general cover through induction from a given set of positive and negative examples of a concept, which may describe all the positive examples, but reject all the negative examples of that concept. Extension matrix theory, a branch of learning from examples, presents the training examples as two matrices which consist of positive and negative examples respectively, and then finds a path satisfied with a positive example against the background of negative examples. Obviously, such a path is a conjunction of conditions that is satisfied by the positive example. However, most of all of pattern recognition problems, such as handwritten Chinese character recognition, have an overlay area. It is necessary to solve the problem of nonlinear boundaries among different classes when machine learning is applied to acquire the recognition rules. An advanced extension matrix algorithm is proposed in this paper, in which a heuristic search based on the average entropy is used to get the approximate solutions of the minimal complex. A potential function is used to estimate the probability density function of the overlay area between positive and negative examples, so that the nonlinear interfaces of the interclass areas may be obtained. This algorithm is applied to supervised learning for Chinese character recognition to acquire recognition rules, and the implementations upon 751,000 loosely-constrained handwritten Chinese characters indicate that these methodologies can be applied to a practical recognition system with promising results.

Supervised learning methods have been recently exploited to learn gene regulatory networks from gene expression data. They consist of building a binary classifier from feature vectors composed by expression levels of a set of known regulatory connections, available in public databases (eg. RegulonDB, TRRD, Transfac, IPA), and using such a classifier to predict new unknown connections. The input to a binary supervised classifier consists normally of positive and negative examples, but usually the only available information are a partial set of gene regulations, i.e. positive examples, and unlabeled data which could include both positive and negative examples. A fundamental challenge is the choice of negative examples from such unlabeled data to make the classifier able to learn from data. We exploit the known topology of a gene network to select such negative examples and show whether such an assumption benefits the performance of a classifier.

Two studies were conducted to determine the relationship between systematic stimulus variation and the formation of misconceptions. Experiment 1 compared five levels of stimulus variation between positive and negative examples, and Experiment 2 compared two levels of stimulus variation among positive examples. Misconceptions were fewer with minimal variation between positive and negative examples and maximal relevant variation among positive examples.

Research has been devoted in the past few years to relevance feedback as an effective solution to improve performance of content-based image retrieval (CBIR). In this paper, we propose a new feedback approach with progressive learning capability combined with a novel method for the feature subspace extraction. The proposed approach is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. Positive examples are used to estimate a Gaussian distribution that represents the desired images for a given query; while the negative examples are used to modify the ranking of the retrieved candidates. In addition, feature subspace is extracted and updated during the feedback process using a principal component analysis (PCA) technique and based on user's feedback. That is, in addition to reducing the dimensionality of feature spaces, a proper subspace for each type of features is obtained in the feedback process to further improve the retrieval accuracy. Experiments demonstrate that the proposed method increases the retrieval speed, reduces the required memory and improves the retrieval accuracy significantly.

Automatic concept learning from large scale imbalanced data sets is a key issue in video semantic analysis and retrieval, which means the number of negative examples is far more than that of positive examples for each concept in the training data. The existing methods adopt generally under-sampling for the majority negative examples or over-sampling for the minority positive examples to balance the class distribution on training data. The main drawbacks of these methods are: (1) As a key factor that affects greatly the performance, in most existing methods, the degree of re-sampling needs to be pre-fixed, which is not generally the optimal choice; (2) Many useful negative samples may be discarded in under-sampling. In addition, some works only focus on the improvement of the computational speed, rather than the accuracy. To address the above issues, we propose a new approach and algorithm named AdaOUBoost (Adaptive Over-sampling and Under-sampling Boost). The novelty of AdaOUBoost mainly lies in: adaptively over-sample the minority positive examples and under-sample the majority negative examples to form different sub-classifiers. And combine these sub-classifiers according to their accuracy to create a strong classifier, which aims to use fully the whole training data and improve the performance of the class-imbalance learning classifier. In AdaOUBoost, first, our clustering-based under-sampling method is employed to divide the majority negative examples into some disjoint subsets. Then, for each subset of negative examples, we utilize the borderline-SMOTE (synthetic minority over-sampling technique) algorithm to over-sample the positive examples with different size, train each sub-classifier using each of them, and get the classifier by fusing these sub-classifiers with different weights. Finally, we combine these classifiers in each subset of negative examples to create a strong classifier. We compare the performance between AdaOUBoost and the state-of-the-art methods on TRECVID 2008 benchmark with all 20 concepts, and the results show the AdaOUBoost can achieve the superior performance in large scale imbalanced data sets.

A bagging SVM to learn from positive and unlabeled examples

Various relevance feedback techniques have been applied in content-based image retrieval. However, many are either heuristics-based, or computationally too expensive to be implemented in real-time, or limited to deal with only positive examples. We propose a fast and optimal linear relevance feedback scheme that takes both positive and negative examples from the user. This scheme can be regarded as a generalization of discriminant analysis on one hang, and on the other hand, it is also a generalization of an existing optimal scheme that takes only positive examples. We first define biased classification problem for the case where the data samples are labeled as positive or negative as to whether belonging to the target class (the biased class) or not; then biased discriminant analysis (BDA) is proposed as an optimal linear solution for dimensionality reduction. We also propose biased whitening transformation on the data when Euclidean distance is applied afterwards. Toy problems are designed to show the theoretical advantages of the proposed scheme over traditional discriminant analysis. It is implemented in real-time image retrieval for large databases and experimental results are presented to show the improvement achieved by the new scheme.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper summarizes the research on discriminative language modeling focusing on its application to automatic speech recognition (ASR). A discriminative language model (DLM) is typically a linear or log-linear model consisting of a weight vector associated with a feature vector representation of a sentence. This flexible representation can include linguistically and statistically motivated features that incorporate morphological and syntactic information. At test time, DLMs are used to rerank the output of an ASR system, represented as an N-best list or lattice. During training, both negative and positive examples are used with the aim of directly optimizing the error rate. Various machine learning methods, including the structured perceptron, large margin methods and maximum regularized conditional log-likelihood, have been used for estimating the parameters of DLMs. Typically positive examples for DLM training come from the manual transcriptions of acoustic data while the negative examples are obtained by processing the same acoustic data with an ASR system. Recent research generalizes DLM training by either using automatic transcriptions for the positive examples or simulating the negative examples.KeywordsAutomatic speech recognitionDiscriminative trainingLanguage modeling

Typically, a (Boolean) query is a finite formula that defines a possibly infinite set of database instances that satisfy it (positive examples), and implicitly, the set of instances that do not satisfy the query (negative examples). We investigate the following natural question: for a given class of queries, is it possible to characterize every query with a finite set of positive and negative examples that no other query is consistent with. We study this question for twig queries and XML databases. We show that while twig queries are characterizable, they generally require exponential sets of examples. Consequently, we focus on a practical subclass of anchored twig queries and show that not only are they characterizable but also with polynomially-sized sets of examples. This result is obtained with the use of generalization operations on twig queries, whose application to an anchored twig query yields a properly contained and minimally different query. Our results illustrate further interesting and strong connections between the structure and the semantics of anchored twig queries that the class of arbitrary twig queries does not enjoy. Finally, we show that the class of unions of twig queries is not characterizable.

The growing availability of online text has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Some IE systems use knowledge learned by single-concept learning systems, as sets of IE rules. Most of such systems need both sets of positive and negative examples. However, the manual selection of positive examples can be a very hard task for experts, while automatic methods for selecting negative examples can generate extremely large example sets, in spite of the fact that only a small subset of them is relevant to learn. This paper briefly describes a more portable multi-concept learning system and presents a methodology to select a relevant set of training examples.KeywordsLearning SystemInformation ExtractionTerminal NodeTraining CorpusWord Sense DisambiguationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Description Logic (DL) Class Expression Learning (CEL) is a recent research topic of interest in the field of machine learning. Given a set of positive and negative examples of individuals in an ontology, the learning problem consists of finding a new class expression or concept such that most of the positive examples are instances of that concept, whereas the negatives examples are not. Therefore, the class expression learning can be seen as a search process in the space of concepts. In this chapter, the use of CEL algorithms is proposed as a tool to find the class expression that describes as much of the instances of positive documents as possible, being the main novelty of the proposal that the ontology is focused on inferring knowledge at syntactic level to determine the orientation of opinion. Furthermore, the use of CEL algorithms can be an alternative to complement other types of classifiers for sentiment analysis, incorporating such description classes as relevant new features into the knowledge base. To do so, an ontology-based text model for the representation of text documents is presented. The process for the ontology population and the use of the class expression learning of sentiment concepts are also described. To show the usefulness and effectiveness of our proposal, we use a set of documents about positive feedback focused on films to learn the positive sentiment concept and to classify the documents, comparing the results obtained against the result obtained by a C4.5 decision tree classifier, using the standard bag of words structure. Finally, we describe the problems that have arisen and solutions that have been adopted in our proposal.

We present a method for synthesizing regular expressions for introductory automata assignments. Given a set of positive and negative examples, the method automatically synthesizes the simplest possible regular expression that accepts all the positive examples while rejecting all the negative examples. The key novelty is the search-based synthesis algorithm that leverages ideas from over- and under-approximations to effectively prune out a large search space. We have implemented our technique in a tool and evaluated it with non-trivial benchmark problems that students often struggle with. The results show that our system can synthesize desired regular expressions in 6.7 seconds on the average, so that it can be interactively used by students to enhance their understanding of regular expressions.

One of the most fundamental problems in computational learning theory is the problem of learning a finite automaton A consistent with a finite set P of positive examples and with a finite set N of negative examples. By consistency, we mean that A accepts all strings in P and rejects all strings in N. It is well known that this problem is NP-complete. In the literature, it is stated that NP-hardness holds even in the case of a binary alphabet. As a standard reference for this theorem, the work of Gold from 1978 is either cited or adapted. Nevertheless, the results in Gold's work are stated in terms of Mealy machines, and not in terms of deterministic finite automata (DFAs) as most commonly defined. As Mealy machines are equipped with an output function, they can be more compact than DFAs which accept the same language. We show that the adaptations of Gold's construction for Mealy machines stated in the literature have some issues, and provide a correct proof for the fact that the DFA-consistency problem for binary alphabets is NP-complete.

As practitioners of machine learning in the area of bioinformatics we know that the quality of the results crucially depends on the quality of our labeled data. While there is a tendency to focus on the quality of positive examples, the negative examples are equally as important. In this opinion paper we revisit the problem of choosing negative examples for the task of predicting protein-protein interactions, either among proteins of a given species or for host-pathogen interactions and describe important issues that are prevalent in the current literature. The challenge in creating datasets for this task is the noisy nature of the experimentally derived interactions and the lack of information on non-interacting proteins. A standard approach is to choose random pairs of non-interacting proteins as negative examples. Since the interactomes of all species are only partially known, this leads to a very small percentage of false negatives. This is especially true for host-pathogen interactions. To address this perceived issue, some researchers have chosen to select negative examples as pairs of proteins whose sequence similarity to the positive examples is sufficiently low. This clearly reduces the chance for false negatives, but also makes the problem much easier than it really is, leading to over-optimistic accuracy estimates. We demonstrate the effect of this form of bias using a selection of recent protein interaction prediction methods of varying complexity, and urge researchers to pay attention to the details of generating their datasets for potential biases like this.