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Results for “sparse learning”

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At least 145 records · Page 8Linked to original sources
PubMed2000

On automatic identification of upper-limb movements using small-sized training sets of EMG signals.

We evaluate the performance of a variety of neural and fuzzy networks for discrimination among three planar arm-pointing movements by means of electromyographic (EMG) signals, when learning is based on small-sized training sets. The aim of this work is to underline the importance that the sparse data problem has in designing pattern classifiers with good generalisation properties. The results indicate that one of the proposed fuzzy networks is more robust than the other classifiers when working with small training sets.

Arm↗
PubMed1994

Back-propagation and counter-propagation neural networks for phylogenetic classification of ribosomal RNA sequences.

A neural network system has been developed for rapid and accurate classification of ribosomal RNA sequences according to phylogenetic relationship. The molecular sequences are encoded into neural input vectors using an n-gram hashing method. A SVD (singular value decomposition) method is used to compress and reduce the size of long and sparse n-gram input vectors. The neural networks used are three-layered, feed-forward networks that employ supervised learning paradigms, including the back-propagation algorithm and a modified counter-propagation algorithm. A pedagogical pattern selection strategy is used to reduce the training time. After trained with ribosomal RNA sequences of the RDP (Ribosomal Database Project) database, the system can classify query sequences into more than one hundred phylogenetic classes with a 100% accuracy at a rate of less than 0.3 CPU second per sequence on a workstation. When compared to other sequence similarity search methods, including Similarity Rank, Blast and Fasta, the neural network method has a higher classification accuracy at a speed of about an order of magnitude faster. The software tool will be made available to the biology community, and the system may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.

Algorithms↗
PubMed2004

Predictive models for protein crystallization.

Crystallization of proteins is a nontrivial task, and despite the substantial efforts in robotic automation, crystallization screening is still largely based on trial-and-error sampling of a limited subset of suitable reagents and experimental parameters. Funding of high throughput crystallography pilot projects through the NIH Protein Structure Initiative provides the opportunity to collect crystallization data in a comprehensive and statistically valid form. Data mining and machine learning algorithms thus have the potential to deliver predictive models for protein crystallization. However, the underlying complex physical reality of crystallization, combined with a generally ill-defined and sparsely populated sampling space, and inconsistent scoring and annotation make the development of predictive models non-trivial. We discuss the conceptual problems, and review strengths and limitations of current approaches towards crystallization prediction, emphasizing the importance of comprehensive and valid sampling protocols. In view of limited overlap in techniques and sampling parameters between the publicly funded high throughput crystallography initiatives, exchange of information and standardization should be encouraged, aiming to effectively integrate data mining and machine learning efforts into a comprehensive predictive framework for protein crystallization. Similar experimental design and knowledge discovery strategies should be applied to valid analysis and prediction of protein expression, solubilization, and purification, as well as crystal handling and cryo-protection.

Bayes Theorem↗
PubMed2004

Neural mechanisms of vocal sequence generation in the songbird.

Little is known about the biophysical and neuronal circuit mechanisms underlying the generation and learning of behavioral sequences. Songbirds provide a marvelous animal model in which to study these phenomena. By use of a motorized microdrive to record the activity of single neurons in the singing bird, we are beginning to understand the circuits that generate complex vocal sequences. We describe recent experiments elucidating the role of premotor song-control nucleus HVC in the production of song. We find that HVC neurons projecting to premotor nucleus RA each generate a single burst of spikes at a particular time in the song and may form a sparse representation of temporal order. We incorporate this observation into a working hypothesis for the generation of vocal sequences in the songbird, and examine some implications for song learning.

Animals↗
PubMed1991

Optimising synaptic learning rules in linear associative memories.

Associative matrix memories with real-valued synapses have been studied in many incarnations. We consider how the signal/noise ratio for associations depends on the form of the learning rule, and we show that a covariance rule is optimal. Two other rules, which have been suggested in the neurobiology literature, are asymptotically optimal in the limit of sparse coding. The results appear to contradict a line of reasoning particularly prevalent in the physics community. It turns out that the apparent conflict is due to the adoption of different underlying models. Ironically, they perform identically at their co-incident optima. We give details of the mathematical results, and discuss some other possible derivations and definitions of the signal/noise ratio.

Animals↗
PubMed2000

Knowledge-based analysis of microarray gene expression data by using support vector machines.

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Algorithms↗
PubMed2003

Child with De Novo t(1;6)(p22.1;p22.1) translocation and features of ectodermal dysplasia with hypodontia and developmental delay.

We report on a 6.5-year-old girl with a balanced translocation between the short arms of chromosomes 1 and 6. She was referred for genetics evaluation because of developmental speech delay and congenital absence of several deciduous and permanent teeth. She was very sensitive to noise (hyperacusis), had poor hair and nail growth, decreased sweating, and turned very red with high fever. She had microcephaly (head circumference at the second centile; weight and height were at 25th centile), short palpebral fissures, epicanthal folds, sparse eyelashes, large ears, partial anodontia, short finger and toenails, and dry skin. She had mild developmental delay. Family history was significant for learning problems in two paternal uncles, one paternal aunt, and several paternal cousins. Thyroid studies, calcium, phosphorus, and alkaline phosphatase levels were normal. Her karyotype was 46,XX,t(1;6)(p22.1;p22.2), and parental karyotypes were normal. This apparently balanced translocation may have resulted in either a submicroscopic loss or disruption of a gene or genes involved in ectodermal dysplasia. There are no reported cases of ectodermal dysplasia associated with this chromosome rearrangement.

Anodontia↗
PubMed2002

An ultra-sparse code underlies the generation of neural sequences in a songbird.

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the 'grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.

Action Potentials↗
PubMed2006

Sparse representation for coarse and fine object recognition.

This paper offers a sparse, multiscale representation of objects. It captures the object appearance by selection from a very large dictionary of Gaussian differential basis functions. The learning procedure results from the matching pursuit algorithm, while the recognition is based on polynomial approximation to the bases, turning image matching into a problem of polynomial evaluation. The method is suited for coarse recognition between objects and, by adding more bases, also for fine recognition of the object pose. The advantages over the common representation using PCA include storing sampled points for recognition is not required, adding new objects to an existing data set is trivial because retraining other object models is not needed, and significantly in the important case where one has to scan an image over multiple locations in search for an object, the new representation is readily available as opposed to PCA projection at each location. The experimental result on the COIL-100 data set demonstrates high recognition accuracy with real-time performance.

Algorithms↗
PubMed2004

Health insurance--a challenge in India.

In India, indemnity health insurance started about 3 decades ago. Mediclaim was the most popular product. Indian insurers and multinational companies have not been enthusiastic about starting health insurance in spite of the availability of a good market because health insurers have historically incurred losses. Losses have been caused by poor administration. Because it is a small portion of their total businesses, insurers have never tried sincerely to improve deficiencies or taken special interest. Hospital management and medical specialists have the spirit of entrepreneurship and are prepared to learn quickly and follow managed care principles, though they are not currently practiced in India. Actuarial data from the health insurance industry is sparse, but data from alternative sources will be helpful for starting managed healthcare. In my opinion, if properly administered, a "limited" managed care product with appropriate precautions and premium levels will be successful and profitable and will compete with present indemnity products in India.

Actuarial Analysis↗
PubMed2004

What can we learn from psychoanalysis and prospective studies about chemically dependent patients?

Despite the common occurrence of drug abusers in the psychoanalytic clinic, contemporary literature on the subject, particularly among publications in the IJP, is sparse. This paper aims to review the most important psychoanalytic contributions on drug dependency in the past 100 years, then attempts to compare their postulations to the findings of pertinent prospective studies. In these patients, a persistent symbiotic object relationship is found, which ties them to narcissistic functioning, where drug use is viewed in the light of both pleasure without object and omnipotently controlled need. The author also discusses the possible contribution of the mother and father in the genesis of this condition, focusing on the compromise of the paternal function as the deciding factor. The theoretical and technical implications of this approach are illustrated by clinical material.

Adult↗
PubMedDate not supplied

Mutual information of sparsely coded associative memory with self-control and ternary neurons.

The influence of a macroscopic time-dependent threshold on the retrieval dynamics of attractor associative memory models with ternary neurons ¿-1, 0, +1¿ is examined. If the threshold is chosen appropriately as a function of the cross-talk noise and of the activity of the memorized patterns in the model, adapting itself in the course of the time evolution, it guarantees an autonomous functioning of the model. Especially in the limit of sparse coding, it is found that this self-control mechanism considerably improves the quality of the fixed-point retrieval dynamics, in particular the storage capacity, the basins of attraction and the information content. The mutual information is shown to be the relevant parameter to study the retrieval quality of such sparsely coded models. Numerical results confirm these observations.

Association Learning↗
PubMed2006

Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning.

'Grid cells' in the dorsocaudal medial entorhinal cortex (dMEC) are activated when a rat is located at any of the vertices of a grid of equilateral triangles covering the environment. dMEC grid cells have different frequencies and phase offsets. However, cells in the dentate gyrus (DG) and hippocampal area CA3 of the rodent typically display place fields, where individual cells are active over only a single portion of the space. In a model of the hippocampus, we have shown that the connectivity from the entorhinal cortex to the dentate granule cells could allow the dentate granule cells to operate as a competitive network to recode their inputs to produce sparse orthogonal representations, and this includes spatial pattern separation. In this paper we show that the same computational hypothesis can account for the mapping of EC grid cells to dentate place cells. We show that the learning in the competitive network is an important part of the way in which the mapping can be achieved. We further show that incorporation of a short term memory trace into the associative learning can help to produce the relatively broad place fields found in the hippocampus.

Action Potentials↗
PubMedDate not supplied

A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models.

A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.

Database Management Systems↗
PubMed2004

A novel algorithm for scalable and accurate Bayesian network learning.

Bayesian Networks (BN) is a knowledge representation formalism that has been proven to be valuable in biomedicine for constructing decision support systems and for generating causal hypotheses from data. Given the emergence of datasets in medicine and biology with thousands of variables and that current algorithms do not scale more than a few hundred variables in practical domains, new efficient and accurate algorithms are needed to learn high quality BNs from data. We present a new algorithm called Max-Min Hill-Climbing (MMHC) that builds upon and improves the Sparse Candidate (SC) algorithm; a state-of-the-art algorithm that scales up to datasets involving hundreds of variables provided the generating networks are sparse. Compared to the SC, on a number of datasets from medicine and biology, (a) MMHC discovers BNs that are structurally closer to the data-generating BN, (b) the discovered networks are more probable given the data, (c) MMHC is computationally more efficient and scalable than SC, and (d) the generating networks are not required to be uniformly sparse nor is the user of MMHC required to guess correctly the network connectivity

Algorithms↗
PubMed1991

[Psychosocial aspects of sexuality in the elderly woman].

In the doctor's office, counseling in matters of sexuality in the aged is rare, and scientific data are sparse. Speculative literature is of little use. Normative ideas from earlier decades need to be checked for their present-day validity. Sexuality as a learning process and form of communication is relatively constant, so that few generalizations are possible. A widely accepted theory is that of non-use, which means in analogy to the mental faculties in aging a steady decline of sexual functions. The possibilities for sexual activity in age are impaired not only by illness and the side effects of drugs; rather, the difference in life expectancy in men and women is also becoming ever more relevant. Thus, today, 1/7 of the 70-75 men are widowed compared with 50% of the women of the same age--three times as many. By the year 2000, 15% of the population will be aged 85 or older. The expected ratio of women to men is 2:1. Elderly women suffer from depression that lasts for weeks about three times as often as men. Possible consequences in the sexual area should be considered.

Aged↗
PubMedDate not supplied

Is the early visual system optimised to be energy efficient?

This paper demonstrates that a representation which balances natural image encoding with metabolic energy efficiency shows many similarities to the neural organisation observed in the early visual system. A simple linear model was constructed that learned receptive fields by optimally balancing information coding with metabolic expense for an entire visual field in a 2-stage visual system. The input to the model consists of a space variant retinal array of photoreceptors. Natural images were then encoded through a bottleneck such as the retinal ganglion cells that form the optic nerve. The natural images represented by the activity of retinal ganglion cells were then encoded by many more 'cortical' cells in a divergent representation. Qualitatively, the system learnt by optimising information coding and energy expenditure and matched (1) the centre surround organisation of retinal ganglion cells; (2) the Gabor-like organisation of cortical simple cells; (3) higher densities of receptive fields in the fovea decreasing in the periphery; (4) smaller receptive fields in the fovea increasing in size in the periphery; (5) spacing ratios of retinal cells; and (6) aspect ratios of cortical receptive fields. Quantitatively, however, there are small but significant discrepancies between density slopes which may be accounted for by taking optic blur and fixation induced image statistics into account. In addition, the model cortical receptive fields are more broadly tuned than biological cortical neurons; this may be accounted for by the computational limitation of modelling a relatively low number of neurons. This paper shows that retinal receptive field properties can be understood in terms of balancing coding with synaptic energy expenditure and cortical receptive fields with firing rate energy expenditure, and provides a sound biological explanation of why 'sparse' distributions are beneficial.

Animals↗
PubMed2006

Support for an auto-associative model of spoken cued recall: evidence from fMRI.

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

Adult↗