There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

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

We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions.

From few to many: illumination cone models for face recognition under variable lighting and pose

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.

Neural network-based face detection

State-of-the-art techniques for writer identification have been centered primarily on enhancing the performance
of the system for writer identification. Machine learning algorithms have been used extensively to improve
the accuracy of such system assuming sufficient amount of data is available for training. Little attention has
been paid to the prospect of harnessing the information tapped in a large amount of un-annotated data. This
paper focuses on co-training based framework that can be used for iterative labeling of the unlabeled data
set exploiting the independence between the multiple views (features) of the data. This paradigm relaxes the
assumption of sufficiency of the data available and tries to generate labeled data from unlabeled data set along
with improving the accuracy of the system. However, performance of co-training based framework is dependent
on the effectiveness of the algorithm used for the selection of data points to be added in the labeled set. We
propose an Oracle based approach for data selection that learns the patterns in the score distribution of classes
for labeled data points and then predicts the labels (writers) of the unlabeled data point. This method for
selection statistically learns the class distribution and predicts the most probable class unlike traditional selection
algorithms which were based on heuristic approaches. We conducted experiments on publicly available IAM
dataset and illustrate the efficacy of the proposed approach.

An Oracle-based co-training framework for writer identification in offline handwriting

Minutiae based fingerprint matching algorithms are wildly used in fingerprint identification and verification applications. However, they may suffer from spurious matches because they do not use the rich local image information. In this paper, we extend minutiae based methods to incorporate such local image information. Our method uses local mutual information, a proven similarity measure in various applications, to improve the matching rate. The overall minutiae distribution pattern between two fingerprints is represented by the initial minutiae matching result, while the mutual information measures the similarity between neighborhoods of matched minutiae, thus enhancing the final matching decision. FVC2002 DB1 and DB3 databases are used to test the proposed approach. Experimental result shows the improvement when combining minutiae matching scores with mutual information scores.

Integrating minutiae based fingerprint matching with local mutual information

Hand-written text recognition

In recent years there has been a growing interest in digitizing the extensive amounts of books and documents that existed preceding the widespread adoption of digital technologies. Many of these digitizing initiatives deal with huge collections of handwritten documents, for which document image analysis techniques (page segmentation, keyword-spotting, optical character recognition (OCR), etc) are not yet as mature as for printed text. Thus, there is an imminent need to develop techniques to understand, archive, index and search the manuscripts. The antiquated approach of manually transcribing handwritten collections and then using standard text retrieval techniques can be very expensive for large collections. But many of the manuscripts in these collections, unlike machine-printed texts, contain unstructured information, cluttered group of texts and graphics that do not necessarily follow a pre-specified format, thus making it quite challenging to automatically process. Thus, in this paper we present a convolutional neural network (CNN) based implementation that is used to segment pages of handwritten documents into their constituent sections. We showcase a multiscale sliding window based network that is trained to predict the sections of the pages in handwritten manuscripts. The results of the network are post-processed with a novel region growing technique to further improve the segmentation results. The implementation is applied on the Marianne Moore archival collection, a body of handwritten notes and memos by the renowned author Marianne Moore (1887–1972), one of the foremost modernist poets of the early twentieth-century. We present our segmentation results both quantitatively and qualitatively.

Segmentation of highly unstructured handwritten documents using a neural network technique

In biometric systems, people may be asked to provide multiple scans for redundancy and quality control. In the case of fingerprint matching systems, repeat fingerprint probes of the same physical finger can be available and data from such multiple samples can be fused for reliable authentication of individuals. Since multiple samples are from the same instance of the finger, some relationships between them, e.g. diversity or similarity, could be observed. In this paper, we investigate such relationships and use them in fusion in order to improve the performance of biometric systems. The relationships between samples are derived by measuring the similarity between matching score vectors with Pearson's correlation and cosine similarity measures. We conduct experiments using the FVC2002 dataset consisting of four fingerprint databases and trainable combination methods, likelihood ratio and multilayer perceptron. The results show that utilization of similarity measures for matching scores can further improve the multi-sample biometric fusion in both combination methods.

/pdf/utilization-of-matching-score-vector-similarity-measures-in-36j6ic7twa.pdf

Utilization of matching score vector similarity measures in biometric systems

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