A Bayesian Algorithm for In Vitro Molecular Evolution of Pattern Classifiers

Zhang, Byoung-Tak; Jang, Ha-Young

doi:10.1007/11493785_39

Cited by 16 publications

(10 citation statements)

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“…Through molecular recognition, the matching molecules are selected and copied. This process increases the density of the molecular memory fragments proportional to the frequency of observations in the data set [89]. In a supervised learning regime where there is a target output associated with the input, the molecules that match with the input but mismatch with the target output are removed from the library.…”

Section: B Learning Hypernets By Molecular Evolutionmentioning

confidence: 99%

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Zhang

2008

IEEE Comput. Intell. Mag.

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Recent interest in human-level intelligence suggests a rethink of the role of machine learning in computational intelligence. We argue that without cognitive learning the goal of achieving human-level synthetic intelligence is far from completion. Here we review the principles underlying human learning and memory, and identify three of them, i.e., continuity, glocality, and compositionality, as the most fundamental to human-level machine learning. We then propose the recently-developed hypernetwork model as a candidate architecture for cognitive learning and memory. Hypernetworks are a random hypergraph structure higher-order probabilistic relations of data by an evolutionary self-organizing process based on molecular selfassembly. The chemically-based massive interaction for information organization and processing in the molecular hypernetworks, referred to as hyperinteractionism, is contrasted with the symbolist, connectionist, and dynamicist approaches to mind and intelligence. We demonstrate the generative learning capability of the hypernetworks to simulate linguistic recall memory, visual imagery, and language-vision crossmodal translation based on a video corpus of movies and dramas in a multimodal memory game environment. We also offer prospects for the hyperinteractionistic molecular mind approach to a unified theory of cognitive learning.

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Section: B Learning Hypernets By Molecular Evolutionmentioning

confidence: 99%

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Zhang

2008

IEEE Comput. Intell. Mag.

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“…None of these deterministic models is able to deal with uncertain knowledge. Other enzyme free models (but not autonomous) have been presented implementing stochastic paradigms [10,24].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic reasoning with a Bayesian DNA device based on strand displacement

Murieta

Rodríguez‐Patón

2013

Nat Comput

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Abstract. We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes' Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro.

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“…The probabilistic update procedure has been proposed by Zhang and Jang [1], [2]. Our method basically follows the same procedure, which is motivated from in vitro evolution [18], [19], [20].…”

Section: Evolutionary Learning Methods For Improved Classificatimentioning

confidence: 99%

“…In automatic text classification using machine learning techniques, the classifiers are learned using training documents and then assign labels to new documents. Due to the properties of document set, the text classification have the following issues [12], [13], [14]: (1) High-dimensional feature space. If distinct words occurring in the training documents are all used, text classification problems with a few thousand training examples can lead to 30,000 and more attributes.…”

Section: Introductionmentioning

confidence: 99%

“…In the library, training examples are encoded as DNA molecules by certain rules, and the frequency of molecules is maintained to be proportional to the probability of observed features from the examples. The probabilistic library model [1], [2] is based on this idea. The molecular library can be used as a probabilistic classifier by maintaining the library to represent the joint probability distribution of training examples and their class.…”

Section: Introductionmentioning

confidence: 99%

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Text Classifiers Evolved on a Simulated DNA Computer

Kim

Heo²,

Zhang³

2006 IEEE International Conference on Evolutionary Computation

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Abstract-The use of synthetic DNA molecules for computing provides various insights to evolutionary computation. A molecular computing algorithm to evolve DNA-encoded genetic patterns has been previously reported in [1], [2]. Here we improve on the previous work by studying the convergence behavior of the molecular evolutionary algorithm in the context of text classification problems. In particular, we study the error reduction behavior of the evolutionary learning algorithm, both theoretically and experimentally. The individuals represent decision lists of variable length and the whole population takes part in making probabilistic decisions. The evolutionary process is to change each individual towards correct classification of training data, which is based on an error minimization strategy. The evolved molecular classifiers show a performance competitive to the standard algorithms such as naïve Bayes and neural network classifiers on the data set we studied. The possibility of molecular implementation by use of DNA-encoded individuals combined with simple molecular operations on a very big population distinguishes this approach from other existing evolutionary algorithms.

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A Bayesian Algorithm for In Vitro Molecular Evolution of Pattern Classifiers

Cited by 16 publications

References 16 publications

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Probabilistic reasoning with a Bayesian DNA device based on strand displacement

Text Classifiers Evolved on a Simulated DNA Computer

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